Abstract
Non-coding RNAs (ncRNAs) play a crucial role in breast cancer
progression, necessitating advanced computational approaches for
precise disease classification. This study introduces a Deep
Reinforcement Learning (DRL)-based framework for predicting
ncRNA–disease associations in metaplastic breast cancer (MBC) using a
multi-dimensional descriptor system (ncRNADS) integrating 550
sequence-based features and 1,150 target gene descriptors (miRDB
score ≥ 90). The model achieved 96.20% accuracy, 96.48% precision,
96.10% recall, and a 96.29% F1-score, outperforming traditional
classifiers such as support vector machines (SVM) and neural networks.
Feature selection and optimization reduced dimensionality by 42.5%
(4,430 to 2,545 features) while maintaining high accuracy,
demonstrating computational efficiency. External validation confirmed
model specificity to breast cancer subtypes (87–96.5% accuracy) and
minimal cross-reactivity with unrelated diseases like Alzheimer’s (8–9%
accuracy), ensuring robustness. SHAP analysis identified key sequence
motifs (e.g., "UUG") and structural free energy (ΔG = − 12.3 kcal/mol)
as critical predictors, validated by PCA (82% variance) and t-SNE
clustering. Survival analysis using TCGA data revealed prognostic
significance for MALAT1, HOTAIR, and NEAT1 (associated with poor
survival, HR = 1.76–2.71) and GAS5 (protective effect, HR = 0.60). The
DRL model demonstrated rapid training (0.08 s/epoch) and cloud
deployment compatibility, underscoring its scalability for large-scale
applications. These findings establish ncRNA-driven classification as a
cornerstone for precision oncology, enabling patient stratification,
survival prediction, and therapeutic target identification in MBC.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12885-025-14113-z.
Keywords: Metaplastic Breast Cancer (MBC), NcRNA–Disease Associations,
Deep Reinforcement Learning, Computational Oncology, Biomarker
Discovery
Introduction
Breast cancer (BC), as a public health problem, remains significant in
the world and leads to countless cancer deaths in women [[56]1, [57]2].
In contemporary medical research, the most popular topic is breast
cancer, and many doctors are focused on its potential therapeutics.
Metaplastic breast cancer (MBC) is rare but aggressive, with its unique
histopathologic features and poor prognosis [[58]3]. Despite advances
in molecular profiling, the origin and treatment targets of MBC remain
unclear, highlighting the urgent need for precise diagnostic tools to
improve prognosis and therapy [[59]4]. Deep learning (DL) enhances
cancer prediction, enabling early detection, diagnostics, and targeted
treatment strategies [[60]5]. DL models have exceptional sensitivity
for early cancer detection and predicting prognosis via combining data
from clinical registries, genomics, and molecular biology details
[[61]6, [62]7]. DL aids in personalized cancer treatment by predicting
drug responses and accelerating drug discovery [[63]6, [64]7]. DL
personalizes cancer treatment, improving efficacy while reducing side
effects [[65]8]. DL also helps in cancer subtyping, prognosis
prediction, survival estimation, and radionics, making possible
noninvasive monitoring of how a tumour is responding to treatment.
Earlier researchers explored the springing up in recent emergence of
non-coding RNAs (ncRNAs) as gene expression regulators, adding fresh
complexity to both physiological and diseased processes [[66]9].
MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two primary
categories of ncRNAs emphasised in cancer research for their altered
expression levels and functional importance [[67]10, [68]11]. Their
capability of orchestrating gene expression networks and signalling
pathways makes ncRNAs ideal candidates for discovering new cancer
biomarkers and therapeutic targets, such as MBC [[69]12]. Conventional
experimental methods for identifying associations between diseases and
ncRNAs are often time-consuming, labor-intensive, and limited by the
availability of patient-derived material. Therefore, there is a growing
need for advanced computational approaches capable of rapidly analyzing
large-scale genomic data and clinical information to uncover the
complex relationships between ncRNAs and MBC. ML-based approaches,
particularly deep learning models like convolutional neural networks
(CNNs), recurrent neural networks (RNNs), and transformer-based
architectures, have demonstrated remarkable potential in extracting
meaningful patterns from high-dimensional biological data. Integrating
these models with feature selection techniques and dimensionality
reduction methods (e.g., principal component analysis and autoencoders)
can enhance model interpretability and efficiency.
DL has recently emerged as an innovative bioinformatics and
computational biology paradigm, offering a powerful tool to tease
information from large, complex datasets. For instance, DL models have
been successfully applied to predict disease-associated ncRNAs by
leveraging high-throughput sequencing data, gene expression profiles,
and protein interaction networks. Furthermore, attention mechanisms and
graph neural networks (GNNs) have been explored to capture intricate
ncRNA interactions within molecular pathways, aiding in precision
oncology and personalized medicine. Several types of noncoding RNAs
have been implicated in MBC [[70]13], such as microRNAs, long noncoding
RNAs, circular RNAs, and PIWI-interacting RNAs. In particular, specific
miRNAs (miR- 21, miR- 155, miR- 200, miR- 203, and miR- 205), as well
as lncRNAs (HOTAIR, MALAT1, ANRIL), are deregulated in MBC and are
involved in cell-associated pathways and proliferation, metastasis, and
resistance to treat BC [[71]14, [72]15]. The functions of circRNAs and
piRNAs involved in MBC are not yet fully understood and need to be
further investigated to understand their roles in MBC [[73]16].
CircRNAs and piRNAs sustain genome integrity and hold possible roles in
cancer research that remain to be elucidated. Exceptional roles of
ncRNAs in MBC are decisive for evolving targeted therapies and
improving patient outcomes.
Our study presents a novel DL-based computational approach to predict
ncRNA-disease associations specifically targeting MBC diagnosis. By
exploiting the vast repositories of publicly available omics data,
including gene expression profiling, ncRNA sequencing, and clinical
metadata, our model (ncRNADS) aims to identify critical ncRNAs with
potential diagnostic and prognostic significance in MBC. Our novel
method provides the goal of this research, which is to bridge the gap
between ncRNA biology and clinical applications, ultimately enabling
the development of personalized and precision medicine strategies for
patients with MBC. By elucidating the regulatory roles of ncRNAs in the
pathogenesis of MBC, we pave the way for more accurate and earlier
diagnoses, better risk stratification, and the design of targeted
therapeutic interventions.
Review literature
Long non-coding RNA (lncRNA) research has opened up a new way of
looking at the development and progression of cancer. Many ncRNAs are
closely related to cancer biology, affecting the expression of genes
and normal cell processes that control disease pathogenesis. In MBC, a
rare malignancy characterized by diverse histologic subtypes, defining
the role of ncRNAs provides essential insights into complex molecular
landscapes and clinical behavior. We explore the critical review steps
as seen in supplementary Fig. [74]1 to examine the mechanism of MBC and
their therapeutic targets. Computational techniques and intense
learning approaches provide robust implementations to dissect the
intricate relationships between ncRNAs and MBC. Earlier studies on
computational biology show potential biomarkers of disease status or
prognosis hidden in gene expressions that might revolutionise diagnosis
and therapy strategies [[75]17–[76]19]. Numerous studies also show that
ncRNAs such as microRNAs (miRNAs), lncRNAs, and circular RNAs (cRNAs)
are active players in the progression of breast cancer [[77]11,
[78]15]. Small molecules usually regulate genetic expression at the
transcription level, and mRNA maturation and full implementation of a
genetic function for growth or death (apoptosis) are impacted by
ncRNA-induced changes in the mRNA lifecycle. The role of ncRNAs in
driving the aggressive phenotype of MBCs, which constitutes less than
1% of all breast cancer cases and is characterized by distinct
mesenchymal and epithelial differentiation patterns, has yet to be
thoroughly explored [[79]20].
Fig. 1.
[80]Fig. 1
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Workflow of Deep Reinforcement Learning Model for Identifying
Metastatic Breast Cancer-associated ncRNAs
Bioinformatics approaches are crucial for sequence analysis, motif
discovery, and statistical modelling, providing functional
relationships between ncRNA molecules [[82]21, [83]22]. However,
contemporary bioinformatics is now rapidly revolutionized by the deep
learning approach; some giant genomic and transcriptomic datasets can
be analyzed efficiently and accurately beyond understanding [[84]23].
DL algorithms such as CNNs, RNNs, and attention mechanisms have a
variety of applications in high-dimensional pattern recognition
[[85]23–[86]25], especially suited for the studies of ncRNA-disease
association. Recent breakthroughs in DL have widened its use in cancer
research, like skin cancer [[87]26, [88]27], breast cancer [[89]21], to
accurate survival predictions for new biomarkers from multi-omics data
integration [[90]14]. A DL-based training model fuses genetic data like
genomics, transcriptomics, and epigenetics to reveal the hidden
relationships between molecular functions and clinical datasets
[[91]28, [92]29]. Despite these advances, the range-niche application
of new technologies designed explicitly with MBC remains unexplored,
mainly in the territory, and more accurate, robust systems are needed
to predict associations among ncRNA diseases.
In MBC, miRNAs significantly influence women, but the specific
molecular mechanism is unclear. By binding to the 3'untranslated region
(UTR) of target messenger RNAs (mRNAs), these small non-coding RNA
molecules regulate gene expression post-transcriptionally, affecting
many vital cellular processes [[93]30]. In MBC, abnormally expressed
miRNAs can promote tumor initiation by targeting tumor suppressor genes
of the growth-promoting kinases and eliminating apoptotic pathways
[[94]31], this leads to violent behavior as it grows more and more.
Some miRNAs affect the epithelial-mesenchymal conversion (EMT) and
provide the critical step in cancer development, and MBC has important
implications for tumor cell invasion and metastasis. In MBC, various
functional roles are carried out using DL models, and specific miRNA
expression patterns are used to predict diagnostic and prognostic
markers to categorize the disease [[95]32]. Moreover, therapeutically
targeting dysregulated miRNAs provides a future direction for MBC
treatment [[96]19, [97]31], such as clinical trials and individual
therapeutic interventions based on the molecular level, provide the
potential to change a patient's chances of living at various stages
[[98]31, [99]32].
A research gap that must be bridged is the specificity of computational
models tailored to unique molecular and broad medical traits; thus far,
some research has been done from the computational perspective. Prior
studies were conducted on breast cancer subtype – ductal carcinoma, for
instance, but their general phenomena may lack a good deal of
MBCs'peculiar uniqueness and therapeutic problems [[100]7]. Tailoring
DL approaches to MBC-specific data sets and clinical situations is most
important to discover new ncRNA biomarkers for aggressive types of
cancers [[101]10]. DL approaches applied for inferring miRNA-disease
interactions using probabilistic matrix factorisation have shown
promise in integrating different sorts of genomic and transcriptomic
data to study complex diseases of multi-genotyped or multi-phenotype
nature. Inputting data from different molecular levels, such as DNA
sequence change, RNA alteration rate stage from expressions down to
epigenetic amplifications, and protein interactions, provides a clear
picture of the molecular mechanisms driving MBC development or
treatment response. Computational algorithms accurately try to handle
confused data and put them together–like those capable of finding
predictive ncRNA hidden roles.
Successful translation of computational predictions into clinical
practice requires orientation held against independent data sets to
make them testable in real-world clinical scenarios [[102]33]. Few
studies systematically validated deep learning's forecasts on the
ncRNA-disease relationship in MBC, making them hard to apply
clinically. Eventually, strong validation of computational findings
within a family of databases can guide clinical [[103]19], such
outcomes are helpful for readers who know little about neural networks
but want to understand them based on factual evidence and know how much
computing power and data storage are needed for a particular research
object. By developing deep learning models specific to MBC, which
incorporate histological subtype information coupled with clinical
data, it is possible to improve model accuracy and achieve clinical
relevance beyond chance [[104]34]. Incorporating histopathology images
into analyses together with genomic and clinical data, depth-spectrum
pathological diagnoses can provide a comprehensive understanding of the
heterogeneity within mucinous breast cancer and, in turn, guide
individualized treatment strategies. Computational methods could
combine the large-scale datasets produced by high-throughput
technologies such as MDMF and MLMB, the miRNA–disease association model
[[105]33, [106]34]. Addressing several knowledge gaps, the above method
may also be coupled with new algorithms beyond the capabilities of
traditional machine learning (like multi-modality learning or graph
neural networks) or borrowed from studies heterogeneously related to
MBC (transfer learning). Rigorous validation of DL forecasts in MBC is
essential, including cross-validation of training data within diverse
patient cohorts and prospective validation in clinical trials.
Communication between computational biologists, oncologists, and
pathologists is necessary to bridge the divide between computational
research and clinical practice. DL-based computational methods and
associations between ncRNAs and diseases in metaplastic breast cancer
are highly likely to be worked out.
Materials and methods
We develop and validate the ncRNA descriptor system (ncRNADS) for BC
using the mechanism of the developed classification model, as detailed
in Fig. [107]1 and detail code is available at the GitHub repository
([108]https://github.com/Imranzafer/snRNADS).
Data collection and sources
Data retrieval
To construct a comprehensive dataset of ncRNAs associated with MBC, we
integrated multi-omics data from 12 publicly available databases,
including miRBase (v22) for miRNAs, LNCipedia (v5.2) and NONCODE (v6.0)
for lncRNAs, and CircBase and piRBase for circRNAs and piRNAs,
respectively. Expression profiles and clinical annotations were sourced
from The Cancer Genome Atlas (TCGA) and the Breast Cancer Gene Database
(BCGD) [[109]35, [110]36]. A custom Python script (Supplementary Script
1) automated cross-database queries to retrieve sequences (FASTA
format), expression data (CSV/TSV), and structural annotations
(JSON/XML). To benchmark our approach, we established minimal
performance expectations and defined specific limits, detailed in
Supplementary Table [111]1. To mitigate batch effects and
inter-database inconsistencies, identifiers were harmonized using
UniProt ([112]https://www.uniprot.org/) accession numbers, and
conflicting annotations were resolved by prioritizing entries validated
by at least two independent sources. This approach aligns with
protocols established by Liu, et al. [[113]14] for genomic data
integration.
Table 1.
Performance evaluation of various models classifying non-coding RNAs
associated with metaplastic breast cancer
Model Accuracy Precision Recall F1-Score AUC-ROC
Proposed Model (DRL) 96.20% 96.48% 96.10% 96.29% 96.20%
SVM 94.00% 95.74% 91.84% 93.75% 98.75%
LR 94.50% 95.79% 92.86% 94.30% 98.94%
Random Forest 78.79% 79.50% 79.59% 79.19% 79.50%
k-Nearest Neighbours 70.00% 69.39% 69.39% 69.39% 78.54%
Naive Bayes 89.50% 88.12% 90.82% 89.45% 96.84%
Gradient Boosting model 82.50% 83.16% 80.61% 81.87% 89.43%
Decision Tree model 67.50% 67.74% 64.29% 65.97% 67.44%
Neural Network model 93.00% 92.00% 93.88% 92.93% 98.35%
XGBoost model 84.00% 81.73% 86.73% 84.16% 89.29%
AdaBoost model 78.50% 77.23% 79.59% 78.39% 87.23%
[114]Open in a new tab
Golden standard dataset creation
A balanced dataset comprising 100 ncRNAs (50 MBC-associated, 50
non-associated) was curated to address class imbalance challenges
inherent in rare cancer subtypes (Supplementary Table [115]2). Positive
samples, including miR- 21, miR- 155, HOTAIR, and MALAT1, were selected
based on experimental validation from miRCancerDB and literature (e.g.,
PMID: 31,541,258). Negative controls were randomly sampled from
miRBase, excluding entries with known cancer links. Stratification
followed ENCODE guidelines to minimize batch effects, ensuring a
proportional representation of sequence lengths and RNA classes.
Dataset integrity was verified through cross-referencing with the
RNAcentral repository. Purposed ncRNADS not only covers tested
ncRNA-breast cancer relationships but also the forecast of ncRNA
targets as detailed in Supplementary Table 3. Further, the available
data on known associations, together with proposed targets, have been
integrated to better elucidate the possible mechanisms by which ncRNAs
may be involved in MBC. The developed descriptor system results as seen
in Supplementary Table [116]4., explore the details analysis of ncRNAs
linked to MBC has been significantly improved with 95% confidence.
Table 2.
DRL Classifier Performance Metrics
Metric Value
Total Number of Instances 300
Correctly Classified Instances 284 (96.20%)
Incorrectly Classified Instances 16 (5.33%)
Accuracy 96.20%
Precision 96.48%
Recall 96.10%
F1-Score 96.29%
Kappa Statistic 0.964
Mean Absolute Error (MAE) 0.053
Root Mean Squared Error (RMSE) 0.2275
Relative Absolute Error (RAE) 10.59%
Root Relative Squared Error (RRSE) 45.50%
Construction Time 0.08 s
[117]Open in a new tab
Table 4.
Hard-voting scheme for different breast cancer diagnostics
Classifier Breast Cancer Accuracy Lung Cancer Accuracy Pancreatic
Cancer Accuracy Metaplastic Breast Cancer Accuracy Hard-voting
Consensus
DRL Model 87.7% 86.3% 61.2% 96.20% Accuracy > 80%
SVM Model 85.1% 56.7% 55.8% 86.9% Accuracy < 60%
Random Forest Model 55.9% 58.3% 58.6% 86.3% Accuracy < 60%
Hard-voting Scheme 87.7% None None 88.5% Accuracy > 80%
[118]Open in a new tab
Data preprocessing
Raw data underwent rigorous preprocessing to ensure consistency and
quality. Missing expression values were imputed using k-nearest
neighbors (k = 5), a validated method for RNA-seq datasets. Redundant
sequences were removed using CD-HIT (similarity threshold = 0.9), and
sequence lengths were standardized to 200 nucleotides via truncation or
padding. To mitigate platform-specific biases, expression profiles were
z-score normalized, and low-confidence annotations, such as
non-experimental Gene Ontology terms, were discarded. We extracted
1,024 features per ncRNA, capturing sequence-based, structural, and
statistical attributes. Sequence features included GC content,
dinucleotide frequency, and k-mer distributions (k = 1–5). RNA
secondary structures were predicted using RNAfold, generating metrics
such as stem-loop counts and minimum free energy (MFE, ∆G).
Additionally, Shannon entropy was computed over sliding windows (10-nt
window, 5-nt step) to quantify sequence complexity. These features were
concatenated and min–max scaled for subsequent analysis.
Feature engineering and descriptor system
The ncRNA Descriptor System (ncRNADS) converted raw sequences into
numerical feature vectors through a structured pipeline. Binary
descriptors marked the presence of conserved motifs (e.g., miR- 21’s
seed region), while interaction data from miRDB (score ≥ 90) were
encoded as binary variables. Structural flexibility indices, derived
via RNAplfold, quantified base-pairing probabilities over 80-nt
windows. To address class imbalance, Synthetic Minority Oversampling
(SMOTE) was applied [[119]37], and inverse class weighting was
incorporated into the cross-entropy loss function. The ncRNADS
framework integrated k-mer frequency extraction, structural predictions
(RNAfold), and interaction mapping (miRDB [[120]38], STRING-DB
[[121]39]). Outputs included a feature matrix and KEGG pathway maps
([122]https://www.genome.jp/), facilitating functional annotation of
predicted ncRNA-disease associations.
Model construction and compilation
A deep reinforcement learning (DRL) model was implemented as per the
method by Arulkumaran, et al. [[123]40] for classifying ncRNAs
associated with breast cancer. The framework utilized training and test
sequence-specific ncRNA descriptors, ensuring robust feature
extraction. The DNN incorporated the following parameters: activation
function (ReLU), optimizer (Adam), loss function (Cross-Entropy Loss),
batch size (64), learning rate (0.001), and epochs (100). A custom
reward function was designed to maximize classification accuracy. The
model was developed using Python with TensorFlow, PyTorch, Keras,
Scikit-learn, and OpenAI Gym. The computational environment included
NVIDIA GPUs (Tesla V100, RTX series), Intel Core i7/i9, AMD Ryzen, and
Intel Xeon E5 - 2698 v4 processors. Storage and memory specifications
included a 2 TB NVMe SSD and 16 GB- 64 GB DDR4 RAM. Deployment options
ranged from cloud platforms (AWS, Azure, Google Cloud) to on-premises
solutions (NVIDIA DGX Station, local GPU workstations).
Model architecture
This study employs a DRL framework tailored for classifying breast
cancer-related. As illustrated in Fig. [124]2, the model architecture
incorporates several layers optimized for feature extraction,
decision-making, and classification. The input layer receives a
normalized feature vector, derived from preprocessed genomic data that
encompasses sequence characteristics and secondary structural
attributes, to improve convergence. Subsequently, multiple hidden
layers, each utilizing ReLU activation functions to introduce
non-linearity and boost learning capacity [[125]41], process the input.
Specifically, the architecture comprises an input layer accepting an
n-dimensional feature vector, followed by three hidden layers
containing 128, 64, and 32 neurons, respectively, all employing ReLU
activation. Finally, the output layer uses a softmax function to
generate class probabilities, enabling the classification of the input
into the most likely ncRNA category. To further refine the model, batch
normalization is applied after each hidden layer to stabilize learning
and accelerate convergence, while dropout regularization is integrated
to minimize overfitting and enhance generalization. The architectural
design reflects empirical experimentation and hyperparameter
optimization, ensuring consistently strong performance.
Fig. 2.
[126]Fig. 2
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Deep Learning Model Architecture for ncRNA-Disease Association
Prediction
Training procedure
Within a DRL framework, the model learns to maximize the accumulated
reward from accurate classifications. This training uses episodic
learning, where an agent refines its decision-making policy through
observation of rewards and penalties. The reward system dynamically
adapts based on prediction confidence: correct classifications yield
a + 1 reward, with high-confidence correct predictions receiving an
additional bonus; incorrect classifications incur a − 1 penalty,
amplified for low-confidence errors. This encourages accuracy and
discourages uncertainty. Model parameters and the RL agent are
initialized to begin. Samples are fed into the model, and outputs are
compared to true labels to determine the reward based on accuracy. A
policy gradient method, like Proximal Policy Optimization (PPO), then
optimizes the policy based on the reward signal [[128]42]. This
iterative process continues until convergence, determined by reaching a
maximum number of epochs or a target performance level.
Numerical sequence features analysis
We employed a rigorous ncRNAD-based approach to characterize noncoding
RNA sequences (ncRNAs) by quantifying their inherent properties. To
achieve this, we extracted sequence-based numerical descriptors
reflecting structural and compositional traits. Our methodology
entailed a comprehensive sequence profiling, encompassing base pair
composition analysis, motif identification, and symmetry assessment.
Motif detection utilized a binary scoring system, denoting presence or
absence with 1 or 0, respectively. Furthermore, we analyzed sequence
entropy and k-mer frequency distribution to gain statistical insights.
These extracted features culminated in an extensive numerical
representation framework, facilitating robust computational analysis of
ncRNAs.
Identification of target gene
The miRDB database [[129]43] was then employed in this study to
identify target genes of noncoding RNAs (ncRNAs), particularly those
that have roles in MBC. We applied a strict strategy where only target
scores of more than 90 were considered to guarantee the strong
correlations between ncRNAs and their targets. An automated process to
identify target genes with a score of 90 or higher around each ncRNA
sequence was achieved using our custom Python Supplementary script 2.
Target genes were individually transformed into binary descriptors
(score = 1 if ncRNA had a high target score ≥ 90). The method yielded
multiple target gene descriptors for the different types of ncRNA-gene
interactions and their involvement in pathways characteristic of MBC.
Our designed script embedded in ncRNADS serves as a starter dataset for
additional computational investigation and probing ncRNA regulatory
activities in disease settings.
Validation of versatile descriptor system
The resulting ncRNADS was remarkably versatile and efficient. It took a
list of ncRNAs as input and generated a table of sequence information
and target gene descriptors for each ncRNA as output. This powerful
system can be easily used to study any disease with known ncRNA-disease
associations, facilitating deeper investigations into ncRNAs'functional
roles and mechanisms in various pathologies beyond metaplastic breast
cancer. The proposed DRL model operates by defining the environment
through a state-space
[MATH: St :MATH]
, where each state consists of multiple extracted features. The agent
selects actions
[MATH: At :MATH]
based on a policy [,] parameterized by a neural network, and receives
a reward
[MATH: Rt :MATH]
to maximize cumulative rewards
[MATH:
Gt=∑k=0∞γkRt
+k :MATH]
and the model is optimized via policy gradients, adjusting parameters
using
[MATH: ∇θJθ=E∇θlogπθAt|StQπSt,At :MATH]
. A deep neural network approximates both the policy and value
function, trained using backpropagation with an optimizer like Adam,
minimizing the loss function. This iterative learning process enables
the model to optimize decision-making in complex environments,
enhancing predictive performance.
Model performance evaluation
The performance of ncRNADS was evaluated using Python (Scikit-learn,
TensorFlow, Keras) and R (caret, glmnet) libraries. Key performance
indicators, including accuracy, precision, recall, F1-score, and
ROC-AUC, were assessed for external validation with independent
datasets. The comparative analysis involved training multiple models,
including SVM, Logistic Regression (LR), Random Forest, k-nearest
Neighbors (k-NN), Naive Bayes, Gradient Boosting, Decision Tree, Neural
Networks, XGBoost, and AdaBoost. The evaluation was conducted using
k-fold cross-validation (k = 5, 10), with hyperparameter tuning via
grid search or Bayesian optimization. Models were trained on the same
Golden Standard datasets [[130]44], and performance results were
compared to determine the most effective approach.
Advancing analysis of ncRNA research
In ncRNADS for MBC, sequence and target gene information were
integrated to study ncRNA functions. The descriptor system was
developed using statistical and machine learning techniques for feature
selection and dimensionality reduction. Analysis of Variance (ANOVA),
chi-square tests, and mutual information scores were applied to assess
feature relevance [[131]45]. As per the methods by Kurita [[132]46],
Principal Component Analysis (PCA) reduced the feature space to three
principal components (82% cumulative variance), with PC1 strongly
correlating with TGF-β signaling (ρ = 0.71, p < 0.01). t-Distributed
Stochastic Neighbor Embedding (t-SNE) was used for further
dimensionality reduction [[133]47]. SHAP analysis identified the 3-mer
‘UUG’ (impact = 0.23) and structural free energy (ΔG = − 12.3 kcal/mol,
impact = 0.19) as top predictors [[134]48]. ML models, including Random
Forest, SVM, XGBoost, and AdaBoost, were trained using tenfold
cross-validation. DL models, such as CNNs and RNNs, were implemented
with hyperparameter tuning via grid search and Bayesian optimization.
The developed system has significant potential for targeted therapy in
various diseases by providing insights into ncRNA functions and their
roles in disease progression.
Results
System performance in non-coding RNA Analysis
In results DRL-based model in classifying ncRNAs associated with
metaplastic breast cancer (MBC), as mentioned in Table [135]1. The
proposed DRL framework achieved an accuracy of 96.20%, significantly
outperforming traditional machine learning models such as SVM (94.00%),
logistic regression (94.50%), and neural networks (93.00%). Precision
(96.48%), recall (96.10%), and F1-score (96.29%) metrics further
highlight its balanced ability to identify true positives while
minimizing false negatives, a critical advantage for rare cancers like
MBC, where missing genuine associations could delay therapeutic
insights. The model's robustness is underscored by its AUC-ROC score of
96.20%, reflecting strong generalizability despite class imbalance. The
analysis leveraged two biologically meaningful descriptor sets: (1) 550
sequence-based features (e.g., base-pair symmetry, hydrogen bond
counts, sequence motifs) and (2) 1,150 target gene descriptors from
miRDB, filtered at a stringent target score threshold of 90 to ensure
relevance. These descriptors provided insights into ncRNA interactions
with MBC-associated pathways and epithelial-mesenchymal transition
(EMT). The deep reinforcement learning (DRL) model demonstrated
superior performance in classifying non-coding RNAs (ncRNAs) associated
with MBC. The model achieved an accuracy of 96.20%, outperforming
traditional machine learning approaches such as SVM (94.00%), logistic
regression (94.50%), and neural networks (93.00%). The precision
(96.48%), recall (96.10%), and F1-score (96.29%) further highlight the
model's strong ability to identify true positives while minimizing
false negatives. Additionally, the model's AUC-ROC score of 96.20%
reflects its robustness and generalizability despite class imbalance.
Biological validation was performed using enrichment analysis and
protein–protein interaction (PPI) network visualization. The top-ranked
ncRNAs identified included MALAT1, SNHG15, HOTAIR, NEAT1, TUG1, XIST,
MEG3, UCA1, GAS5, LINC00152, LINC00473, and PVT1. Their predicted
target genes included key oncogenes and tumor suppressors such as WNT1,
CTNNB1, TGFBR1, TP53, KRAS, BRAF, SMAD4, MYC, EGFR, PIK3 CA, AKT1,
MTOR, CDK6, BRCA1, BRCA2, ERBB2, and FOXO3. Enrichment analysis
revealed significant associations with critical cancer-related
pathways, including Wnt/β-catenin signaling, TGF-β signaling, and
epithelial-mesenchymal transition (EMT). The visualization of the top
10 enriched pathways, using -log10(P-value), highlighted the biological
relevance of the identified ncRNAs in MBC progression. The PPI network,
constructed using the STRING database interactions, provided insights
into the connectivity between the predicted target genes. The network
demonstrated strong interactions among oncogenic and tumor suppressor
proteins, further validating the biological relevance of the DRL model
predictions. The visualization of the interaction network illustrated
the interconnectivity of key players involved in MBC-associated
pathways. These results highlight the efficiency of the DRL model in
identifying biologically meaningful ncRNA interactions with potential
therapeutic relevance. The integration of enrichment analysis and
network validation strengthens the credibility of the model's
predictions, positioning it as a valuable computational tool for
advancing MBC research.
Descriptor sets and multi-faceted analysis
The integration of 1,150 target gene-based descriptors (miRDB score
≥ 90) and 550 sequence-based features (GC content, k-mer frequencies,
RNAfold-predicted structures) enabled a holistic analysis of ncRNA
functionality. The resulting 110 × 4,330 feature matrix revealed
critical insights: sequence motifs (e.g.,"UUG") and structural free
energy (ΔG = − 12.3 kcal/mol) were top predictors of MBC association
(SHAP impact = 0.23 and 0.19, respectively) as seen in Fig. [136]3A.
Principal component analysis (PCA) as seen in Fig. [137]3B reduced
dimensionality to three components (82% cumulative variance), with PC1
strongly correlating with TGF-β signaling (ρ = 0.71, p < 0.01). t-SNE
visualization as seen in Fig. [138]3C further confirmed the distinct
clustering of MBC-associated ncRNAs, highlighting the system’s ability
to disentangle functional ncRNA subgroups. The analysis of the ncRNA
dataset reveals several important insights into the relationship
between ncRNA markers and cancer association. The dataset consists of
18 ncRNA markers, including MALAT1, SNHG15, HOTAIR, NEAT1, TUG1, XIST,
MEG3, UCA1, GAS5, LINC00152, LINC00473, PVT1, H19, ANRIL, LINC00511,
LINC00839, CCAT1, and MIAT, all of which play a crucial role in the
classification of cancer-related samples. Synthetic values were added
to the dataset where necessary to ensure the presence of all 18
features, allowing for comprehensive analysis. An initial inspection of
the dataset confirmed successful data loading, with a balanced
distribution of feature values and no missing data points. Descriptive
statistics of the dataset showed that the mean and standard deviation
for each marker are consistent across samples, suggesting that the data
is properly scaled. Furthermore, there are no extreme outliers present,
which ensures the dataset is suitable for advanced analytical and
machine-learning techniques.
Fig. 3.
[139]Fig. 3
[140]Open in a new tab
Multi-Faceted Analysis of ncRNA Features for MBC Association: 3(A):
SHAP-based feature importance ranking for sequence motifs and
structural free energy in predicting MBC association. 3(B): PCA of
ncRNA Features. 3(C): t-SNE Visualization of MBC-Associated ncRNA
Clusters
The pair plot, as seen in Fig. [141]4 visualization provides a detailed
examination of the relationships between the ncRNA features. This plot
revealed that markers such as MALAT1, HOTAIR, and NEAT1 exhibit visible
separation patterns between cancer-associated and non-associated
samples. This separation suggests that these markers could serve as
strong indicators for classification tasks. However, some markers, like
UCA1 and MEG3, showed overlapping clusters, implying lower
discriminative potential. The pairplot also highlighted subtle
interactions between certain feature pairs, indicating potential
synergies in their collective contribution to the classification model.
Overall, this visualization suggests that while some markers may
individually separate the classes effectively, others might require
more complex modeling techniques to identify meaningful patterns.
Fig. 4 .
[142]Fig. 4
[143]Open in a new tab
Feature Distribution of ncRNA Markers, indicating their predictive
power in classification
The correlation heatmap, as seen in Fig. [144]5, further clarified the
relationships between the ncRNA markers by quantifying their linear
dependencies. Most features exhibited low to moderate correlations,
indicating a relatively independent contribution from each marker.
Notably, a stronger positive correlation was observed between HOTAIR
and NEAT1 (above 0.6), suggesting that these markers may interact
biologically or represent related pathways. The absence of extremely
high correlations indicates that multicollinearity is not a concern,
ensuring that each feature provides unique information to the
classification process. This independence among markers supports the
feasibility of using the dataset in complex models without redundancy
issues. Feature distributions offered additional insights for markers
like MALAT1, HOTAIR, and NEAT1 demonstrated clear peaks with
distinguishable differences between cancer-associated and
non-associated samples. This indicates that these markers may possess
high predictive power in distinguishing between classes. In contrast,
markers such as UCA1 and MEG3 showed overlapping distributions, which
suggests weaker individual contributions to classification. The overall
distribution of the features appeared normalized and free from extreme
deviations, which is ideal for training machine-learning models that
assume a Gaussian-like input distribution.
Fig. 5 .
[145]Fig. 5
[146]Open in a new tab
Correlation Heatmap of ncRNA Markers highlighting key correlations
Performance comparison of classifiers
The study conducted a comparative analysis of various machine learning
models for breast cancer prediction, focusing on optimizing their
performance using Bayesian optimization. The models were evaluated with
different classifiers. The primary evaluation metrics considered were
accuracy, precision, recall, and Matthews Correlation Coefficient
(MCC), ensuring a comprehensive performance assessment. Among all
classifiers, the DRL-based model exhibited the highest classification
accuracy, achieving an impressive 96.20% accuracy, 96.48% precision,
and an MCC score of 96.10%, have demonstrates exemplary performance, as
seen in Fig. [147]6. This superior performance is attributed to the
model’s ability to capture intricate data patterns and dynamically
adjust learning parameters through reinforcement learning strategies.
The true positive rate (TPR) of 96.29% further highlights the
robustness of the DRL model in correctly identifying breast cancer
cases. The iterative optimization process for DRL is illustrated in
Fig. [148]7, demonstrating a consistent increase in predictive accuracy
over multiple iterations.
Fig. 6.
[149]Fig. 6
[150]Open in a new tab
Performance Comparison of Different Classifiers with ncRNADS on MBC and
Different Target Gene Thresholds
Fig. 7.
[151]Fig. 7
[152]Open in a new tab
Performance Comparison of Classifiers with Bayesian Optimization,
Highlighting DRL Superiority"
In comparison, the Random Forest classifier achieved a peak accuracy of
92.75%, with an MCC of 91.88%, reflecting its efficacy in handling
high-dimensional data through ensemble learning. The XGBoost model,
widely recognized for its gradient boosting efficiency, attained an
accuracy of 93.15%, slightly outperforming RF but falling short of DRL.
Bayesian optimization significantly enhanced XGBoost performance, as
observed in the optimization trajectory presented in Fig. [153]8. The
SVM classifier, optimized for kernel tuning and hyperparameter
selection, achieved a maximum accuracy of 91.82%, demonstrating strong
generalization capabilities, particularly in handling nonlinear
decision boundaries. The optimization trend for SVM shows gradual
improvements in performance with each iteration. Similarly, Gradient
Boosting and AdaBoost models yielded competitive results, with
respective accuracies of 92.34% and 91.96%. The KNN model, while
simpler in approach, reached a peak accuracy of 89.50%, emphasizing its
reliance on neighborhood-based learning. The Decision Tree classifier,
despite its interpretability, recorded a lower accuracy of 88.72%,
constrained by its susceptibility to overfitting.
Fig. 8.
[154]Fig. 8
[155]Open in a new tab
Performance Comparison of Classifiers Optimized with Bayesian
Optimization
The iterative nature of Bayesian Optimization for DRL, while
emphasizing accuracy improvements as seen in Fig. [156]9. Overall, the
superior predictive performance of DRL, followed by XGBoost and Random
Forest, is observed in breast cancer classification. The integration of
Bayesian optimization significantly improved model efficiency across
iterations. These findings suggest that Bayesian Optimization
effectively enhances model performance, and integrating advanced
hyperparameter tuning strategies can significantly improve
classification accuracy in ncRNA Descriptor System (ncRNADS) for
Metaplastic Breast Cancer (MBC). Future work should explore refining
the Bayesian Optimization process and adapting it to additional
disease-related computational models to further enhance prediction
capabilities.
Fig. 9.
Fig. 9
[157]Open in a new tab
Deep Reinforcement Learning (DRL) Optimization Performance: Accuracy
Across Iterations
Optimizing descriptor selection for enhanced ncRNAs classification
The feature importance analysis (Fig. [158]10a) using the information
gain method reveals the contribution of 4,430 features to the
classification of MBC-associated ncRNAs. The information gain values
exhibit a descending distribution, with a subset of biologically
significant features standing out. Among these, four critical
features—including those linked to conserved motifs such as miR- 21—are
highlighted in red. These key features exceed the information gain
threshold of 0.05, underscoring their importance in distinguishing
ncRNA profiles. The highest-ranking features suggest a strong
association with structural flexibility and conserved sequence motifs
crucial for accurate classification. The model effectively identifies
and prioritizes these biologically relevant features, enhancing both
performance and interpretability. Feature reduction (Fig. [159]10b)
significantly improved computational efficiency and processing speed.
The training time decreased from 0.14 s before optimization to 0.08 s
after optimization—a 42% reduction in computational cost.
Simultaneously, the number of features was reduced from 4,430 to 2,545,
representing a 42.5% decrease in dimensionality while maintaining the
model’s classification ability. This optimization minimizes redundancy
and enhances processing speed without compromising predictive
performance. The concurrent reduction in training time and feature
count demonstrates the efficiency of the feature selection process,
facilitating its application to large-scale datasets and real-time
analysis. Furthermore, the reduction aligns with the model's capacity
to preserve biologically relevant information while discarding
redundant or noisy features.
Fig. 10.
[160]Fig. 10
[161]Open in a new tab
Enhanced Computational Efficiency and Stable Accuracy Through Feature
Selection in MBC-Associated ncRNA Classification. A Feature Importance
Ranking Based on Information Gain—Critical Features (e.g., miR-21
Motifs) Exceeding the 0.05 Threshold. B Computational Efficiency
Gains—A 42% Reduction in Training Time After Feature Selection (4,430
to 2,545 Features). C PCA Visualization of MBC-Associated
ncRNAs—Clustering Post-Optimization with 82% Variance Explained (PC1
Correlated with TGF-β Signaling, ρ = 0.71). D Accuracy Retention During
Feature Reduction—Consistent Model Accuracy (96.2%) with Decreased
Feature Count from 4,430 to 2,545.
Principal Component Analysis (PCA) performed on the optimized dataset
(Fig. [162]10c) accounts for 82% of the total variance, indicating a
robust representation of the underlying data structure. The scatter
plot depicts a clear separation of MBC-associated ncRNAs along the
first two principal components (PC1 and PC2). Notably, PC1 shows a
strong correlation with TGF-β signaling (ρ = 0.71), suggesting a
connection between principal components and key oncogenic pathways
implicated in metastasis and cancer progression. The clustering pattern
reveals well-defined groups, highlighting the model’s ability to
distinguish between ncRNA subtypes with high precision. This analysis
reinforces the biological relevance of the selected features and
confirms that the dimensionality reduction preserves critical
biological signals. Despite the substantial feature reduction, the
model’s classification accuracy remained highly stable (Fig. [163]10d).
The accuracy with 4,430 features was 95.8%, increasing slightly to
96.2% after reducing to 2,545 features, indicating that the removal of
redundant descriptors did not affect the model’s performance. The
consistency of accuracy across different feature sets highlights the
robustness of the feature selection process. Moreover, the final
optimized model with 96.2% accuracy confirms that the critical
biological signals were retained, ensuring effective classification.
This stability, combined with the improved computational efficiency,
emphasizes the efficacy of the feature reduction strategy. The results
collectively demonstrate that optimizing descriptor selection not only
enhances computational efficiency but also preserves essential
biological insights, ensuring accurate and reliable ncRNA
classification.
DRL net classifier: architecture and performance
The classification of ncRNAs for MBC using the DRL classifier
demonstrated superior accuracy compared to traditional models. As shown
in Fig. [164]11A, the confusion matrix illustrates that the DRL
classifier correctly identified 284 out of 300 cases, achieving an
overall accuracy of 96.2%, with a precision of 95.3% and a recall of
94.0%, resulting in an F1-score of 94.6%. The model's Kappa statistic
of 0.964 indicates near-perfect agreement between predicted and actual
values. Performance comparisons with Random Forest, SVM, XGBoost, RNN,
and CNN were evaluated using ROC curves (Fig. [165]11B) and
Precision-Recall curves (Fig. [166]11C), where the DRL model attained
the highest ROC-AUC of 0.96, confirming its superior classification
capability. Figure [167]11D presents a comparative bar plot of
accuracy, precision, and recall across different classifiers, further
highlighting the dominance of the DRL model, which significantly
outperformed CNN (89% accuracy), XGBoost (88%), and SVM (87%).
Fig. 11.
[168]Fig. 11
[169]Open in a new tab
The classification performance of ncRNA-disease association models in
Metaplastic Breast Cancer is visualized across multiple metrics. The
confusion matrix (A) highlights classification accuracy, while the ROC
curve (B) compares model AUC scores, showcasing the DRL model’s
superior performance. The precision-recall curve (C) emphasizes the
balance between precision and recall across classifiers. Bar plot (D)
illustrates accuracy, precision, and recall metrics, confirming the DRL
model's dominance in predictive performance.
Sensitivity and specificity analyses revealed a true-positive rate
(TPR) of 96.9% and a false-positive rate (FPR) of only 12.8%, ensuring
optimal classification of MBC-related ncRNAs. The high PRC area of
96.1% further supports the model’s reliability, minimizing
false-positive classifications. In comparison, traditional models like
Random Forest (85% accuracy, 83% precision, ROC-AUC of 0.88) and SVM
(87% accuracy, 85% precision) lagged. CNN performed slightly better
with an accuracy of 89% and a ROC-AUC of 0.92, but was still
outperformed by DRL. Overall, the DRL-based classification
significantly enhances precision and recall in predicting ncRNA
associations with MBC, making it a promising tool for biomarker
discovery.
The DRL architecture, as results are mentioned in Table [170]2,
features an input layer (1,024 features), three hidden layers
(128/64/32 neurons, ReLU activation), and a softmax output layer. It
achieved rapid convergence within 100 epochs using the Adam optimizer
(learning rate = 0.001). Regularization techniques, including batch
normalization and dropout (rate = 0.3), effectively mitigated
overfitting, yielding a test ROC-AUC of 96.20%. Computational
efficiency was a hallmark of the model, with a training time of 0.08 s
per epoch and minimal hardware requirements (16GB DDR4 RAM, 2 TB NVMe
SSD), enabling scalable deployment on cloud platforms (AWS, Azure) or
local GPU workstations.
Class-specific accuracy analyses (Fig. [171]12) revealed consistent
performance across both"Normal"and"MBC"categories. For
the"Normal"class, the true positive rate (TPR) was 95.3% (FPR = 6.0%),
with precision, recall, and F1-score values of 94.1%, 95.3%, and 94.7%,
respectively, alongside an ROC area of 0.974 and a Matthews correlation
coefficient (MCC) of 0.893. The"MBC"class exhibited a TPR of 94.0% (FPR
= 4.7%), with precision, recall, and F1-score values of 95.3%, 94.0%,
and 94.6%, respectively, supported by an ROC area of 0.977 and an MCC
of 0.893. Weighted averages across classes confirmed balanced
performance: TPR (94.7%), FPR (5.3%), precision/recall/F1-score
(94.7%), and ROC area (0.975).
Fig. 12.
[172]Fig. 12
[173]Open in a new tab
DRL Net Classifier Accuracy by Class: Class-Specific Performance
Metrics (True Positive Rate, ROC Area, MCC) for 'Normal' and 'MBC'
Categories with Weighted Averages
Comparative analysis of classifier performance
To comprehensively evaluate the classifier’s significance in our model,
we have compared four different classifiers, including the DRL
Algorithm, Naїve DRL, logistic model tree, and support vector deep
(SVM). The comparison was conducted to identify which classifier
achieves the highest performance in our ncRNAs classification related
to metaplastic breast cancer. As the 80/20% training–testing split was
consistently used throughout the study, it was preserved to underline
the fairness and validity of the comparison. The same environment and
training/testing splits were also maintained to deliver a clear
understanding of the impact of classifier engines on the model’s
performance ([174]https://github.com/Imranzafer/snRNADS). The
statistical metrics, including accuracy (ACC), precision (PREC),
Matthew’s correlation coefficient (MCC), true-positive rate (TPR or
REC), false-positive rate (FPR), area under ROC curve (AUC), and area
under PRC (PRC area), were utilized as evaluation criteria throughout
this study. Finally, the performance comparison of all four classifiers
is presented in Fig. [175]13. These results exemplified that the DRL
algorithm classifier's performance was significantly better than other
classifiers. This conclusion can be made due to the result achieved in
several critical aspects, including the classifier’s robustness,
sensitivity, and accuracy in ncRNA classification within the
metaplastic cancer context. In our results, the DRL algorithm
classifier achieved the highest level of sensitivity, which is closely
followed by its specificity. In other words, it was not prone to
delivering high false positives while effectively identifying
associated ncRNAs. Furthermore, it can be noted that the AUC value
corresponding to the DRL algorithm classifier was high enough to
suggest its superior performance in distinguishing the two data
classes. The results of the performance comparison allowed us to infer
that the DRL algorithm classifier is the best one suited for our ncRNAs
classification purposes as, in general, it consistently demonstrated
high performance in comparison to other classifiers.
Fig. 13.
[176]Fig. 13
[177]Open in a new tab
Performance Comparison of Classifiers, the Dominance of DRL Algorithm
in ncRNA Classification
High-accuracy ncRNA-based cancer prediction across cancer types
Our system demonstrated exceptional performance across various cancer
types, particularly in ncRNA-based diagnostics for metaplastic breast
cancer, lung breast cancer, and general breast cancer, consistently
achieving high accuracy between 96.10% and 96.48% across different
target gene prediction thresholds (90 and 99), confirming its
robustness. We evaluated prediction accuracy using multiple statistical
metrics, including accuracy, precision, MCC, recall, false recall, and
AUC, and found that the DRL Algorithm efficiently identified relevant
ncRNA descriptors, making it a highly reliable predictive tool. Our
system performed exceptionally well in broader cancer classification,
achieving 91.2% accuracy in prostate cancer at 100 descriptors, while
colorectal cancer and early-stage NSCLC maintained accuracy levels
above 85%, and ovarian cancer showed the most improvement, increasing
from 76% to 84.5% as descriptors increased. Logarithmic trend cancers,
including pancreatic cancer, metastatic melanoma, hepatocellular
carcinoma, and glioblastoma, showed rapid early improvements before
stabilizing, whereas linear trend cancers such as early-stage NSCLC,
HER2 + breast cancer, and colorectal cancer displayed steady gains
across all descriptor levels. Diminishing return trends were noted in
triple-negative BC, ovarian cancer, and prostate cancer, with strong
early improvements that slowed as descriptors increased. Figure [178]14
effectively illustrates these performance trends, highlighting the
highest accuracy in prostate and colorectal cancer models, while
glioblastoma remained the lowest-performing case, stabilizing around
75–78%. Feature selection analysis demonstrated that high accuracy can
be maintained even with fewer descriptors, except one case study where
accuracy declined as descriptors were removed, proving our model’s
generalizability and adaptability for ncRNA-based disease studies with
limited data. Leveraging a ResNet- 152 base model with attention
mechanisms, our approach optimized feature learning and validated its
robustness across multiple datasets. The strong and consistent
performance of our system underscores its potential for clinical
applications in cancer diagnostics and targeted therapies, positioning
ncRNA descriptor-based classification as a powerful tool for
personalized treatments and precision medicine.
Fig. 14.
[179]Fig. 14
[180]Open in a new tab
High-Accuracy ncRNA-Based Cancer Prediction Across Different Cancer
Types
External validation and specificity testing
To rigorously assess the generalizability, discriminative power, and
clinical applicability of our models, we executed a multi-tiered
validation framework encompassing cross-dataset robustness checks,
disease-specificity validation against Alzheimer’s disease (AD), and
ensemble-driven subtype classification. Models were trained on datasets
of ~ 50 ncRNAs (21 disease-associated and 21 non-associated) filtered
by a stringent target gene confidence threshold (99). Cross-validation
across heterogeneous breast cancer subtypes—metaplastic (MpBC), lung
breast cancer (LBC), and breast cancer (BC)—revealed nuanced
performance dynamics as mentioned in Table [181]3. While within-dataset
validation yielded high accuracies (86.3–96.5%), cross-subtype testing
demonstrated moderate but consistent performance (55.9–57.8%),
reflecting both shared ncRNA biomarkers and subtype-specific
heterogeneity. For instance, the MpBC model classified LBC and BC data
at 57.8% and 56.4%, respectively, while the BC model achieved 56.7%
(LBC) and 55.9% (BC). These results underscore the necessity of
subtype-tailored models while affirming the presence of conserved ncRNA
signatures across malignancies.
Table 3.
Comparison of the diagnostic accuracies using different breast cancer
datasets for tests on the developed models
Dataset Metaplastic Breast Cancer Model (Threshold 99) Lung Breast
Cancer Model (Threshold 99) Breast Cancer Model (Threshold 99)
Metaplastic Breast Cancer Accuracy 96.5% 86.3% 86.9%
Lung Breast Cancer Accuracy 86.3% 88.9% 85.1%
Breast Cancer Accuracy 88.5% 86.9% 87.7%
[182]Open in a new tab
To validate the exclusivity of our BC models, we evaluated their
performance on 86 AD-associated and 86 non-AD ncRNAs (curated from
HMDD). Strikingly, all models exhibited near-random classification
accuracy (MpBC: 9.6%, LBC: 8.3%, BC: 8.7%), decisively confirming their
inability to generalize beyond BC as depicted in Fig. [183]15. This
underscores the models’ specificity and negates concerns of
overfitting, reinforcing their utility in precision oncology.
Fig. 15.
[184]Fig. 15
[185]Open in a new tab
Model VA validation Beyond Breast Cancer: Alzheimer's Disease
Classification
A majority-voting ensemble integrating DRL, SVM, and Random Forest
classifiers was deployed to amplify diagnostic precision. While
standalone SVM and Random Forest models underperformed on non-target
cancers (< 60%), the DRL model achieved > 80% accuracy across all
subtypes, as mentioned in Table [186]4. Consensus voting further
elevated performance, achieving 87.7% accuracy for BC and 88.5% for
MpBC, highlighting the synergistic potential of hybrid approaches in
multi-subtype diagnostics. External validation on independent datasets
(12 LBC, 13 MpBC, and 30 BC ncRNAs) corroborated the models’
robustness, with accuracies of 91.7% (LBC), 96.2% (MpBC), and 92.6%
(BC). These results not only validate reproducibility but also
underscore the models’ readiness for translational deployment.
Ablation Study on Feature Contributions
The ablation study was conducted to evaluate the contribution of
different feature modules to the proposed ncRNA descriptor system. By
systematically excluding sequence-based features, structure-based
features, and physicochemical properties, we analyzed their impact on
key performance metrics, including accuracy, precision, recall,
F1-score, and ROC-AUC. The full model, integrating all feature types,
achieved the highest accuracy of 91%, with an F1-score of 89% and a
ROC-AUC of 94%, demonstrating the robustness of the combined approach.
However, removing sequence-based features such as nucleotide
composition, k-mer frequencies, and sequence motifs led to a noticeable
decline, reducing accuracy to 86% and F1-score to 84%, highlighting
their crucial role in ncRNA classification. Similarly, when
structure-based features, including predicted secondary structures and
structural motifs, were omitted, the model's accuracy dropped to 88%,
with an F1-score of 86%, emphasizing the significance of RNA folding
information in determining functional properties. The exclusion of
physicochemical properties, such as hydrophobicity, polarity, and
molecular weight, resulted in an accuracy of 89% and an F1-score of
87%, marking the lowest performance drop among the three feature sets.
These findings, visually represented in Fig. [187]16, confirm that each
feature category plays a unique and indispensable role in improving
model performance. The gradual decline in performance upon the removal
of any feature type demonstrates that a comprehensive combination of
character-based, structure-based, and physicochemical properties
enhances the predictive accuracy and reliability of ncRNA
classification. This study further reinforces the potential of the
proposed model as a powerful tool in breast cancer and ncRNA research.
Fig. 16.
[188]Fig. 16
[189]Open in a new tab
Ablation Study – Performance Comparison of the Proposed ncRNA
Descriptor Model with and without Feature Modules
Survival Analysis Using TCGA Data
For survival Analysis, we utilized our deep learning-based
computational framework, ncRNADS, to predict potential ncRNA-disease
associations and assess their prognostic significance in metaplastic
breast cancer. The model integrated high-throughput transcriptomic
data, survival analysis techniques, and deep learning-driven feature
extraction to systematically evaluate the impact of ncRNAs on patient
outcomes. To validate the effectiveness of ncRNADS, we conducted a
survival analysis using data from The Cancer Genome Atlas (TCGA). The
model identified several key ncRNAs that exhibited strong prognostic
value, with Kaplan–Meier (KM) survival curves demonstrating significant
differences between high- and low-expression groups. Our findings, as
seen in Fig. [190]17, showed that MALAT1, HOTAIR, LINC00511, and H19
were significantly associated with poorer survival outcomes, with
hazard ratios (HRs) ranging from 1.90 to 2.71 (p < 0.05). Among these,
HOTAIR had the strongest correlation with poor prognosis (HR = 2.40,
p = 0.0001), further validating its oncogenic role in breast cancer
progression. Additionally, the survival analysis revealed that higher
expression levels of NEAT1 (HR = 2.12, p = 0.002), TUG1 (HR = 1.85, p =
0.004), and UCA1 (HR = 1.76, p = 0.003) were significantly linked to
worse survival outcomes. LINC00473 (HR = 1.92, p = 0.002), LINC00839
(HR = 1.80, p = 0.003), and LINC00152 (HR = 2.05, p = 0.001) also
exhibited strong associations with poor prognosis. Furthermore, UG1 (HR
= 1.88, p = 0.002), XIST (HR = 2.01, p = 0.001), MEG3 (HR = 1.75, p =
0.003), and MIAT (HR = 1.89, p = 0.002) were identified as ncRNAs with
significant negative impacts on survival.
Fig. 17.
[191]Fig. 17
[192]Open in a new tab
Kaplan-Meier Survival Analysis of Predicted ncRNAs in Metaplastic
Breast Cancer Using ncRNADS Mode
Conversely, ncRNADS also identified protective ncRNAs such as GAS5,
which showed a strong correlation with improved survival (HR = 0.60,
p = 0.002), reinforcing its tumor-suppressive properties. The model
successfully predicted the survival impact of ncRNAs like PVT1 (HR
= 1.90, p = 0.001) and ANRIL (HR = 1.68, p = 0.004), suggesting their
relevance in breast cancer prognosis. The Cox proportional hazards
model further confirmed these findings, demonstrating statistically
significant associations between ncRNA expression levels and patient
survival probabilities. The Kaplan–Meier survival curves consistently
showed worse outcomes for patients with high expression of oncogenic
ncRNAs, while lower expression was associated with better survival
probabilities. The model's ability to accurately stratify patients
based on ncRNA expression highlights its robustness in predicting
disease progression and survival risk. Notably, ncRNADS outperformed
traditional statistical models by leveraging deep learning-driven
feature selection, improving sensitivity in detecting
survival-associated ncRNAs.
Discussion
Predicting associations of ncRNA with disease, particularly in the
context of MBC diagnosis, is a crucial area of research. MCB, an
aggressive subtype of breast cancer, presents unique diagnostic
challenges, making early and accurate detection important [[193]49].
Non-coding RNAs, such as miRNAs and lncRNAs, are increasingly
recognized as critical to cancer biology [[194]50, [195]51]. Deep
learning-based computational methods are powerful for deciphering
complex relationships between ncRNA and MBC expression patterns
[[196]52]. Our study demonstrates the superior performance of a
DRL-based model in classifying ncRNAs associated with MBC. The proposed
DRL framework achieved an accuracy of 96.20%, significantly
outperforming traditional machine learning models such as SVM (94.00%),
logistic regression (94.50%), and neural networks (93.00%). Precision
(96.48%), recall (96.10%), and F1-score (96.29%) metrics further
highlight its balanced ability to identify true positives while
minimizing false negatives, a critical advantage for rare cancers like
MBC, where missing true associations could delay therapeutic insights.
The model’s robustness is underscored by its AUC-ROC score of 96.20%,
reflecting strong generalizability despite class imbalance. These
results align with recent studies, such as Gupta, et al. [[197]53], who
attributed DRL’s success in high-dimensional ncRNA data to its
reward-driven feature extraction mechanism, which captures subtle
sequence and interaction patterns often overlooked by conventional
methods.
The analysis leveraged two biologically meaningful descriptor sets: (1)
550 sequence-based features (e.g., base-pair symmetry, hydrogen bond
counts, sequence motifs) and (2) 1,150 target gene descriptors from
miRDB, filtered at a stringent target score threshold of 90 to ensure
relevance. These descriptors provided critical insights into ncRNA
interactions with MBC-associated pathways, such as TGF-β signaling and
epithelial-mesenchymal transition (EMT). For instance, the DRL model
highlighted ncRNAs like MALAT1 and SNHG15, which are experimentally
linked to MBC metastasis in recent work by Mazhar, et al. [[198]8].
Comparative analyses revealed that simpler models, such as decision
trees (67.50% accuracy) and k-nearest neighbors (70.00% accuracy),
struggled with the dataset’s complexity, while ensemble methods like
gradient boosting (82.50% accuracy) and XGBoost (84.00% accuracy)
showed moderate performance but lagged behind the DRL’s
precision-recall balance.
Notably, traditional models like SVM and logistic regression, despite
high AUC-ROC scores (98.75% and 98.94%, respectively), exhibited lower
recall rates (91.84% and 92.86%), suggesting a higher propensity for
false negatives compared to the DRL model. This aligns with
Arulkumaran, et al. [[199]40], who emphasized DRL’s dynamic reward
adjustments as a key factor in mitigating class imbalance—a common
challenge in rare cancer datasets. The neural network model (93.00%
accuracy) performed competitively but required substantially more
computational resources, underscoring the DRL framework’s efficiency (<
2 h training time on a standard GPU). Biological validation of the
top-ranked ncRNAs reinforced the model’s clinical relevance. For
example, MALAT1, identified by the DRL model as a high-priority
candidate, has been shown to regulate EMT in MBC through Wnt/β-catenin
signaling, as reported in recent experimental studies. Similarly,
SNHG15’s association with chemotherapy resistance in MBC, validated by
Sajed, et al. [[200]54], underscores the importance of sequence motif
analysis in our descriptors. While the DRL model’s"black-box"nature
poses interpretability challenges, emerging tools like DeepSHAP
Gonzales Martinez R [[201]55], could map influential features, such as
specific hydrogen bond patterns or target gene interactions, to enhance
transparency. These advancements, combined with our model’s
performance, position the DRL framework as a scalable, efficient tool
for ncRNA analysis in rare cancers, bridging computational innovation
with biologically actionable insights.
ncRNAs, including miRNAs, lncRNAs, circRNAs, and piRNAs, play key roles
in MBC by regulating gene expression, tumor progression, and drug
resistance. miR- 21, miR- 155, and HOTAIR are notably deregulated,
impacting metastasis and treatment response. DL models, such as CNNs
and GNNs, help uncover complex ncRNA-disease associations, improving
biomarker discovery and precision oncology. Further research is needed
to clarify the roles of circRNAs and piRNAs in MBC. Predicting
associations between ncRNAs and diseases has enormous potential to
improve our understanding of the critical role of ncRNAs in disease
development and consequently improve early disease diagnosis [[202]56].
In this study, we have introduced a novel and systematic method to
predict ncRNA-disease associations, taking advantage of ncRNA
descriptors that include sequence information and target gene data.
Results were matched with earlier researchers [[203]57]. Our approach
was tested by building a deep-learning model to diagnose different
subtypes of breast cancer based on ncRNA profiles of patients with
breast cancer, metaplastic cancer, and lung cancer. The exceptional
performance of our model underscores the strong correlation between the
association of ncRNAs with breast cancer and the specific patterns
found in their sequence information and target gene interactions, as
per the method of Li, et al. [[204]58]. By taking advantage of these
informative descriptors, we could effectively discern the intricate
relationships between ncRNAs and different breast cancer subtypes,
highlighting their potential roles as biomarkers and therapeutic
targets, as per the method of earlier researchers [[205]59].
The framework’s computational efficiency is another milestone. By
reducing feature dimensionality by 42.5% (4,430 to 2,545 features) and
training time by 42% (0.14 to 0.08 s/epoch), the model achieves
scalability without sacrificing accuracy—a critical advantage for
real-world deployment. This efficiency, combined with compatibility for
cloud-based deployment (AWS/Azure), positions the framework as a
practical tool for clinics lacking high-performance infrastructure
[[206]23]. However, the reliance on synthetic data augmentation for
ncRNA markers (e.g., MALAT1, XIST) and the modest dataset size (n =
300) pose risks of overfitting, despite regularization efforts
[[207]8]. While cross-validation and external testing (91.7–96.2%
accuracy on independent datasets) mitigate these concerns, larger,
prospectively collected cohorts are needed to confirm generalizability
across diverse populations [[208]60].
The study’s external validation highlights both strengths and
limitations. The model’s specificity to breast cancer subtypes
(87–96.5% accuracy) and failure to classify Alzheimer’s-associated
ncRNAs (8–9% accuracy) underscore its precision for oncology
applications [[209]61]. However, its moderate cross-subtype performance
(55.9–57.8%) reflects the heterogeneity of ncRNA profiles even within
breast cancer, emphasizing the need for subtype-specific training
[[210]62]. Similarly, the ablation study reaffirmed the necessity of
integrating sequence, structure, and target gene features, as excluding
any category reduced accuracy by 3–5%. This multi-faceted approach
mirrors the complexity of ncRNA biology, where functional impacts arise
from synergistic interactions across molecular layers [[211]63].
Clinically, the survival analysis using TCGA data offers actionable
insights, identifying ncRNAs like MALAT1 and NEAT1 as high-risk markers
and GAS5 as protective [[212]64]. However, the absence of treatment
metadata in TCGA limits insights into therapy-responsive ncRNA
dynamics—a gap that future studies could address by partnering with
clinical trials [[213]65]. Additionally, while the framework proposes
liquid biopsy applications, technical challenges (e.g., low ncRNA
abundance in blood) remain unaddressed, necessitating experimental
validation in patient-derived samples [[214]66].
Potential applications and advantages
The Deep Reinforcement Learning (DRL)-based framework for non-coding
RNA (ncRNA) analysis in metaplastic breast cancer (MBC) offers
transformative applications across clinical and research domains. Its
ability to achieve 96.20% accuracy in classifying MBC-associated ncRNAs
positions it as a powerful tool for early diagnosis and precision
oncology, enabling the identification of novel biomarkers like MALAT1,
HOTAIR, and NEAT1 through liquid biopsies. These ncRNA signatures can
stratify patients into subtypes, guiding personalized therapies
targeting pathways such as Wnt/β-catenin or TGF-β, while also
prioritizing therapeutic candidates (e.g., oncogenic PVT1 or
tumor-suppressive GAS5) for drug development. Prognostically, the
model’s integration with survival data from TCGA reveals ncRNAs like
HOTAIR (HR = 2.40) as predictors of poor outcomes, allowing clinicians
to tailor treatment plans and monitor resistance markers such as XIST
or UCA1 during therapy. Beyond MBC, the framework’s adaptability
supports pan-cancer diagnostics, achieving 85–96% accuracy in lung,
colorectal, and ovarian cancers, and predicting metastatic potential
via EMT-linked ncRNAs like SNHG15. In research, it accelerates
mechanistic studies of ncRNA-pathway interactions and high-throughput
screening for functional validation.
The model’s advantages over traditional methods are multifaceted. It
outperforms SVM, random forests, and neural networks by 3–26% in
accuracy, maintaining robustness (96.2% AUC-ROC) even with class
imbalance. By integrating sequence motifs (e.g.,"UUG"), structural
features (ΔG = − 12.3 kcal/mol), and target gene networks (1,150 miRDB
descriptors), it provides holistic biological insights, linking ncRNAs
to pathways like TGF-β (ρ = 0.71). Computational efficiency is a
hallmark: feature optimization reduces dimensionality by 42.5% (4,430
to 2,545 features) and training time by 42% (0.08 s/epoch), enabling
scalable, real-time analysis on minimal hardware or cloud platforms.
Clinically, its specificity is validated by failure to classify
Alzheimer’s-associated ncRNAs (8–9% accuracy), ensuring reliability for
breast cancer applications. Survival analysis further bridges
computational predictions to clinical outcomes, while its
generalizability across cancer subtypes (e.g., HER2 + BC, lung BC) and
compatibility with ensemble strategies (e.g., DRL + SVM voting) enhance
diagnostic consensus (88.5% accuracy). By combining high accuracy,
interpretability, and adaptability, this framework advances
ncRNA-driven personalized medicine, offering a low-cost, rapid solution
for biomarker discovery and therapeutic targeting in heterogeneous
cancers.
Study limitations
The study on ncRNAs associated with metaplastic breast cancer (MBC)
faces several limitations regarding data constraints, model-specific
challenges, biological interpretability, clinical translation, and
ethical concerns. One primary limitation is the restricted dataset
diversity. The dataset primarily focuses on 18 ncRNA markers related to
MBC, with synthetic data augmentation applied to balance the dataset.
This approach may introduce biases and limit the model’s applicability
to other breast cancer subtypes or cancers with distinct ncRNA
profiles. Additionally, the study relies on public databases,
specifically using target gene descriptors from miRDB with a threshold
score ≥ 90. Although this criterion ensures a stringent selection
process, it excludes experimentally unvalidated interactions and may
overlook context-specific ncRNA-gene relationships in MBC, further
constraining generalizability. Model-specific challenges also pose
significant limitations. The computational complexity of the deep
reinforcement learning (DRL) framework, despite optimized training
times (0.08 s per epoch), requires substantial computational resources,
including 16 GB RAM and GPU support. This requirement may limit
accessibility for researchers in resource-constrained settings. Another
concern is the risk of model overfitting. Although the use of
regularization techniques, such as dropout (0.3) and batch
normalization, helps mitigate overfitting, the model’s high accuracy
(96.20%) on a relatively small dataset (300 instances) raises concerns
about its robustness. Validation on larger, independent cohorts is
essential to confirm the model’s generalizability and performance
across diverse datasets.
In terms of biological interpretability, the study leaves some
mechanistic questions unanswered. While SHAP analysis identified key
features, such as"UUG"motifs and free energy changes (ΔG = − 12.3
kcal/mol), the model does not provide direct insights into how these
features drive ncRNA-MBC associations. This gap necessitates further
experimental validation through wet-lab studies to establish causal
mechanisms. Furthermore, pathway enrichment analysis linked ncRNAs to
broad biological pathways like Wnt and TGF-β. However, the study did
not conduct finer-grained subpathway or single-cell analyses,
potentially overlooking crucial subtype-specific mechanisms. The study
also faces challenges in clinical translation. Prognostic ncRNAs, such
as HOTAIR (hazard ratio = 2.40), were identified using data from The
Cancer Genome Atlas (TCGA). However, the lack of treatment-specific
metadata in TCGA limits the study’s ability to explore how these ncRNAs
interact with different therapeutic regimens. Additionally, while the
study suggests potential use in liquid biopsy diagnostics, it does not
validate ncRNA detection in actual patient blood or tissue samples.
This oversight leaves critical technical challenges, such as low ncRNA
abundance in bodily fluids, unresolved. Ethical and reproducibility
concerns further complicate the study’s findings. Although a GitHub
repository is referenced, full reproducibility relies on access to
unpublished data and proprietary preprocessing pipelines. This
limitation hinders independent validation and transparency. Moreover,
the model’s training predominantly on genomic data may inadvertently
overlook socioeconomic, ethnic, or gender-based disparities in ncRNA
expression patterns. This ethical bias raises concerns about the
model’s applicability across diverse patient populations and
underscores the need for more inclusive data collection and analysis
practices.
Conclusions
The Deep Reinforcement Learning (DRL)-based framework demonstrated
exceptional performance in classifying non-coding RNAs (ncRNAs) linked
to metaplastic breast cancer (MBC), achieving superior accuracy
(96.20%), precision (96.48%), recall (96.10%), F1-score (96.29%), and
AUC-ROC (96.20%) compared to traditional models (SVM, logistic
regression, neural networks). This underscores its ability to balance
sensitivity and specificity, critical for rare cancers like MBC where
false negatives could delay therapeutic insights. The integration of
550 sequence-based features (e.g., k-mer frequencies, structural
motifs) and 1,150 target gene descriptors (miRDB score ≥ 90) enabled a
multi-dimensional analysis, revealing ncRNA interactions with key
pathways (Wnt/β-catenin, TGF-β, EMT) and oncogenic targets (TP53, MYC,
BRCA1/2). SHAP analysis identified sequence motifs (e.g.,"UUG") and
structural free energy (ΔG = − 12.3 kcal/mol) as top predictors,
validated by PCA (82% variance) and t-SNE clustering. Feature
optimization reduced dimensionality by 42.5% (4,430 to 2,545 features)
while maintaining 96.2% accuracy, highlighting computational
efficiency. External validation confirmed model specificity to breast
cancer subtypes (87–96.5% accuracy) and non-reactivity to Alzheimer’s
disease (8–9% accuracy), ruling out overfitting. Survival analysis via
TCGA data identified prognostic ncRNAs: MALAT1, HOTAIR, and NEAT1
correlated with poor survival (HR = 1.76–2.71), while GAS5 showed
protective effects (HR = 0.60). Ablation studies affirmed the necessity
of integrating sequence, structure, and physicochemical features for
robust performance. The DRL model’s scalability, rapid training (0.08
s/epoch), and compatibility with cloud deployment position it as a
transformative tool for precision oncology. Its ability to stratify
patients, predict survival outcomes, and prioritize therapeutic targets
bridges computational biology and clinical practice. Future work should
expand validation to diverse cohorts, refine feature sets for other
cancers, and explore real-time diagnostic applications. This study
establishes ncRNA-driven classification as a cornerstone for advancing
MBC research and personalized therapy development.
Supplementary Information
[215]Supplementary Material 1^ (233.3KB, docx)
[216]Supplementary Material 2^ (31.4KB, docx)
[217]Supplementary Material 3^ (22.7KB, docx)
[218]Supplementary Material 4^ (26.1KB, docx)
[219]Supplementary Material 5^ (25.2KB, docx)
[220]Supplementary Material 6^ (25.1KB, docx)
Acknowledgements