Abstract

   With human guidance, computers now use machine learning (ML) in
   artificial intelligence (AI) to learn from data, detect trends, and
   make predictions. Software can adapt and improve with new information.
   Imaging scans leverage pattern recognition to predict outcomes,
   diagnose disorders, and suggest treatments. Tuberculosis (TB) remains
   the most common bacterial disease affecting humans. The World Health
   Organisation reported that in 2022, 1.3 million people died from
   tuberculosis, with the death rate potentially reaching 66% if proper
   treatment isn’t provided. We trained ML-supervised algorithms like XG
   Boost, Logistic Regression, Random Forest Classifier, Ad- aBoost, and
   Support Vector Machine to help classify TB patients from large
   RNA-sequence count data. Such algorithms provided prediction accuracies
   of 0.963, 0.739, 0.773, 0.866, and 0.866 sequentially. This article
   highlights feature importance techniques using the ML model, XGBoost,
   with the highest prediction accuracy of 0.963, identifying significant
   genes in TB RNA sequence count data. Using key machine learning
   features, we here identified 20 pathways, 24 gene ontologies, 20 hub
   genes, and 22 drugs. Next, we applied advanced computational
   techniques, including pathway analysis, GO, hub-protein and
   protein–protein interactions (PPI), transcriptomic and miRNA
   interactions, and drug-protein interactions, to help analyze 100 highly
   expressed genes.

   Keywords: ML, Bioinformatics, TB, DEGs, Gene ontology, PPIs, Hub gene,
   Potential drug

   Subject terms: Computational biology and bioinformatics, Microbiology

Introduction

   Machine Learning (ML) is being more widely utilized across various
   sectors including military, cybersecurity, healthcare, among many more.
   Additionally, ML algorithms are employed to address intricate and
   emerging challenges in biomedical research, encompassing tasks such as
   text mining, drug discovery, single-cell RNA sequencing, as well as
   early diagnosis and prognosis of diseases^[44]1. ML techniques are
   frequently used for analyzing and interpreting a variety of data types,
   including multi-omics, imaging, clinical records, medication details,
   and disease progression. Biomedical data science encompasses a variety
   of data formats, including genome sequences, omics data, medical
   imaging, and clinical records^[45]2. As time progresses, machine
   learning methods are increasingly gaining favor within the medical
   sector for their efficacy in aiding decision-making processes^[46]3.
   The study of RNA-sequence data reveals the effectiveness of machine
   learning algorithms in discovering splice variants within RNA-sequence
   data by utilizing a variety of ML methods for different detection
   objectives and to clarify sequence correlations^[47]4.

   For thousands of years, tuberculosis (TB) has been a common infectious
   illness that has caused high rates of death in several vulnerable
   populations. Despite advances in medical science, this tendency
   continues into the modern period. According to the World Health
   Organization’s most current TB estimated 10.6 million new cases of TB
   and 1.3 million fatalities from the disease in 2022 (World Health -
   organization, 2023). Up to the advent of SARS-CoV-2, tuberculosis (TB)
   was the leading infectious cause of mortality in humans. Approximately
   1.6 million people died from TB in 2021, mostly in low- and
   middle-income countries^[48]5. Early identification, drug resistance
   screening, and thorough treatment with short-course regimens can
   effectively cure tuberculosis^[49]6. Automated techniques for
   tuberculosis diagnosis and classification have improved disease
   identification precision, enabling healthcare professionals to make
   more informed decisions.

   Additionally, innovators in advanced biotechnology have created a
   number of bioinformatics tools that have facilitated the investigation
   of illness studies. Many researchers have employed machine learning
   (ML) algorithms to predict chronic and viral diseases^[50]7,[51]8
   including the prevalence of tuberculosis based on associated risk
   factors^[52]9,[53]10. Within these fields of artificial intelligence
   (AI), machine learning (ML) builds mathematical models using training
   data to make precise diagnoses and choices without the need for
   explicit manual programming for specific tasks.

   We therefore used an XGBoost-based feature importance approach to help
   identify the precise highly expressed genes from RNA sequencing count
   data in this TB patient research. This approach was used instead of
   only relying on the values of Adjusted P-values and Log Fold Changes to
   identify important genes.

   We then developed a classification pipeline for tuberculosis diagnosis
   using RNA-Seq microarray data, enabling rapid and reliable analysis of
   large transcriptome datasets for meaningful conclusions. Before
   proceeding with further analysis, the initial step involves assessing
   the quality of the unprocessed raw sequence data. Machine learning
   algorithms were used to identify significant genes, followed by
   bioinformatics analyses such as pathway enrichment, gene ontology, and
   drug prediction. The biological processes and roles associated with the
   discovered genes, as well as possible therapeutic possibilities for TB
   treatment, are clarified by these investigations. Our work’s visual
   depiction is seen in Fig. [54]1. Future efforts will use machine
   learning algorithms to quickly identify key genes and provide an
   instant prognosis estimate for TB patients based on gene sequence data.
   This will make it easier for medical personnel to handle the problem.
   In conclusion, these findings will be extremely helpful in controlling
   or reducing the hazards related to tuberculosis patients, providing
   invaluable assistance to researchers and medication makers.

Fig. 1.

   [55]Fig. 1
   [56]Open in a new tab

   The suggested approach and the workflow.

Methods and materials

Data sets and pre-processing

   GEO, located at [57]https://www.ncbi.nlm.nih.gov/geo/, is a global
   public archive for genetic and genomic data that enables access to
   large datasets created by scientific communities, such as data from
   microarrays, next-generation sequencing, and other analytical methods.
   GEO makes this essential information more easily shared and accessible
   to researchers around the world^[58]11. We retrieve our dataset from
   GEO with accession ID [59]GSE103147^[60]12. Furthermore, we acquired
   this dataset by analyzing raw count data through “GREIN”, an
   alternative tool facilitating the exploration of publicly available
   gene expression data^[61]13.

   The dataset was assembled utilizing the “Illumina HiSeq 2000 (Homo
   sapiens)” platforms. 6,363 healthy adolescents aged between 12 and 18
   years, were enrolled in the study and followed for 24 months or longer.
   Six months was the period between the collection of blood. Among them,
   41 were diagnosed with active TB, while 104 served as asymptomatic
   control^[62]12. The period between blood collection and the diagnosis
   of active TB referred to as “time to diagnosis”, varied from 1 to 894
   days. Nevertheless, given that it is count data, we obtained 28,091
   genes for each individual and the dataset has dimensions of 28,089 rows
   and 1,608 columns. The count data are presented in tabular form,
   illustrating the number of sequence fragments associated with each gene
   across every sample^[63]14. The overview of the dataset is provided in
   Table [64]1.

Table 1.

   A nutshell of the datasets and findings from the transcriptome study.
   Diseases GEO ID Sequencing platform Tissue Cell Gene of every sample
   Case samples Control samples
   Tuberculosis [65]GSE103147 Illumina HiSeq 2000 Whole blood T cells and
   monocytes 28,091 724 884
   [66]Open in a new tab

   After acquiring the data, we conducted pre-processing by utilizing the
   functionalities provided by the “pandas” library. The “pandas” library
   offers built-in, user-friendly functions for handling routine data
   manipulations and conducting analyses on datasets like this one. Its
   objective is to serve as the fundamental framework for Python’s
   statistical computing endeavors in the foreseeable future^[67]15.

Machine learning algorithms

   This section discusses the experimental use of numerous ML algorithms
   for the prediction of tuberculosis.

Random forest (RF) classifier

   Random Forest, a machine learning algorithm developed by Breiman and
   Cutler, combines many decision trees for both regression and
   classification problems, offering versatility and
   user-friendliness^[68]16. It is a classifier that uses multiple
   decision trees on a dataset, based on the majority votes, to improve
   predictive accuracy and prevent overfitting, resulting in higher
   accuracy^[69]16.

   The Random Forest algorithm is a low-training, high-accuracy prediction
   method that performs efficiently on large datasets. It uses a two-phase
   process, combining N decision trees to create a random forest^[70]17.
   The process involves choosing K data points at random, creating
   decision trees linked to these points, selecting N decision trees, and
   performing the first two steps. The predictions from each tree are then
   allocated to the group with the most votes.

AdaBoost classifier

   AdaBoost is an ensemble boosting classifier, proposed by Freund and
   Schapire since 1996. It combines multiple weak classifiers to increase
   accuracy. AdaBoost is an iterative method that sets classifier weights
   and trains data samples for accurate predictions of unusual
   observations^[71]18. AdaBoost is a classifier that aims to reduce
   training errors in each iteration by increasing the weight of
   incorrectly classified instances using a Decision Tree method. It is
   recommended to train the classifier interactively with a range of
   weighted training samples. However, this technique cannot be
   parallelized as each predictor needs to be trained after the preceding
   one. The AdaBoost algorithm involves assigning each observation the
   same weight, creating a model using a subset of data, computing errors
   by contrasting predicted and actual values, assigning higher weights to
   mistaken data points, and repeating these steps until the error
   function remains unchanged or the maximum number of estimators is
   achieved^[72]19.

Logistic regression classifier

   Logistic regression is a popular machine learning algorithm used to
   predict a categorical dependent variable using independent variables.
   It predicts probabilistic values between 0 and 1, instead of providing
   exact values like 0 and 1^[73]20.

Procedure for logistic regression

   The procedure for implementing Logistic Regression in Python is the
   same as it was in earlier regression topics. The steps are as follows:
    1. Pre-processing of the data
    2. Assessing the training set for logistic regression fit
    3. Projecting the exam outcome
    4. Test the correctness of the outcome (matrix creation)
    5. Showing the test set outcome visually.

Logistic regression equation

   From the linear regression equation, one may get the logistic
   regression equation. The following lists the mathematical procedures to
   obtain equations for logistic regression.

   Here the straight-line equation may be expressed as follows:
   graphic file with name d33e528.gif

   In Logistic Regression y can be between 0 and 1 only, so for this let’s
   divide the above equation by (1−y):
   graphic file with name d33e535.gif

   But we need a range between − [infinity] to +[infinity], then take the
   logarithm of the equation it will become:
   graphic file with name d33e542.gif

   This classification algorithm is used to classify linear logarithms.
   However, it can be defined in this way:
   graphic file with name d33e549.gif

XGBoost classifier

   XGBoost is a gradient-boosting machine learning algorithm, renowned for
   its computational efficiency, feature importance analysis, and handling
   of missing values, used in regression, classification, and ranking
   tasks. Dr. Chen from Washington University introduces XGBoost, an
   ensemble learning model based on Boosting. It constructs multiple CART
   trees based on splitting nodes, reduces variance, and optimizes CPU
   multi-threads for improved accuracy. It has applications in artificial
   intelligence, data analysis, statistics, and mining^[74]21.

Support vector machine

   Support Vector Machine (SVM) is a popular Supervised Learning algorithm
   used for classification and regression problems in Machine Learning. It
   aims to create the best decision boundary, a hyperplane, for separating
   n-dimensional space into classes. Vapnik and colleagues developed SVMs
   in the 1990s and presented their findings in 1995^[75]22.

Support vector regression

   Support vector regression (SVR) is a continuous regression method that
   extends linear SVMs, primarily used for time series prediction, by
   finding a hyperplane with the maximum margin between data points,
   unlike linear regression which requires specifying relationships
   between variables.

Differential gene expression

   DGE analysis is a crucial bioinformatics technique for understanding
   biological processes by comparing gene expression levels between
   conditions or treatments. Differential expression analysis involves
   analyzing normalized read count data to identify quantitative changes
   in expression levels between experimental groups through statistical
   analysis. Differential expression testing aims to identify genes
   expressed at different levels between conditions, providing biological
   insights into the processes impacted by the conditions of
   interest^[76]23.

Feature importance

   “Feature importance” is a method that assigns a score to each input
   feature in a model, with higher scores indicating a greater influence
   of the characteristic on the model used to forecast a variable^[77]24.

   Understanding the importance of a feature can improve a model’s clarity
   and effectiveness by providing insights into the relationships between
   input and output variables. This can help remove unnecessary or
   redundant features and identify important qualities that may have gone
   unnoticed, opening up new research possibilities. Methods like Gradient
   Boosting and XGBoost feature importance can be used to determine
   feature importance, offering diverse perspectives on feature
   significance and identifying the most influential ones in a model’s
   predictions^[78]25.

Molecular pathway enrichment and gene ontology analysis

   A framework and a collection of ideas for characterizing the roles of
   gene products from all species are provided by the Gene Ontology
   (GO)^[79]26. The Gene Ontology (GO) knowledge base is a significant
   resource for understanding gene functions, enabling computational
   analysis of large-scale molecular biology and genetics studies in
   biomedical research, making it both machine- and human-readable.

   A significant introspective effort to categorize biological viewpoints,
   such as biological mechanisms or chromosomal regions linked to a range
   of related disorders, is known as a functional enrichment evaluation. A
   comprehensive web-based tool for gene set enrichment, EnrichR was used
   to study pathway enrichment and gene ontology, which includes
   biological processes, cellular components, and molecular functions.
   several KEGG^[80]27, Reactome^[81]28, WikiPathways^[82]29, Elsevier,
   and BioCarta^[83]30 were utilized to characterise the major pathways,
   with an acceptable adjusted P-value of less than 0.05.

Protein–protein interactions (PPIs) analysis

   Protein–protein interactions (PPIs) are physical connections formed
   through biochemical or electrostatic processes between proteins, which
   are crucial for various biological processes like cell-to-cell
   contacts, cell cycle progression, signal transmission, and metabolic
   pathways.Online software called the Search Tool for the Retrieval of
   Interacting Genes (STRING)^[84]31 is widely used to evaluate PPI data.
   The dataset was utilized to evaluate DEG relationships, and a PPI
   network was created using Cytoscape, revealing significantly correlated
   proteins based on topological characteristics^[85]32.

Hub gene identification

   Hub genes are those in the gene network that collaborate with a large
   number of other genes and are frequently essential for biological
   functions and gene regulation. Fur- thermore, hub genes were shown to
   be the most strongly linked to illness^[86]33.

   A Cytoscape plugin called CytoHubba (Version 0.1)^[87]34 was utilized
   to determine the network hub genes. Another plugin for Cytoscape that
   may be used to generate and display functionally grouped networks of
   biological concepts and pathways is called ClueGO^[88]35. A useful
   addition to ClueGO, the CluePedia Cytoscape plugin searches for
   additional markers that may be connected to routes.

Assessing transcription factors (TF) and miRNAs network

   Proteins known as transcription factors control how genes are copied
   into RNA, which is the first step in the process of creating proteins.
   Transcription factors, are proteins that bind to DNA, and activate or
   deactivate genes, with enhancers and silencers binding sites in
   specific parts of the body, enabling cells to perform logic
   operations^[89]36.

   We utilized the Network Analyst tool to explore the JASPAR^[90]37 and
   ChEA^[91]38 databases and identify topographically plausible
   transcription factors likely to link to our DEGs. JASPAR is a publicly
   available database containing carefully selected, non-redundant
   transcription factor (TF) binding profiles for TFs in many species
   spanning six taxonomic groupings. These profiles are kept as TF
   flexible models (TFFMs) and position frequency matrices (PFMs).

   A gene-set enrichment analysis technique called ChIP-X^[92]39.
   Enrichment Analysis is designed to determine whether query gene sets
   are enriched with genes that may be transcription factor targets. In
   ChEA, sets of probable target genes are selected from published
   ChIP-chip, ChIP-seq, ChIP-PET, and DamID investigations and labeled
   with transcription factors using a gene-set library (Fig. [93]2).

Fig. 2.

   [94]Fig. 2
   [95]Open in a new tab

   Supervised learning model to diagnosis tuberculosis.

   MTIs, or miRNA-target interactions, are stored in the miRTarBase
   database. By using reporter assays, western blots, or microarray
   studies with miRNA overexpression or knockdown, the gathered MTIs are
   experimentally confirmed.

Analysis of potential drugs

   The term “Potential Drug-Drug Interaction” (or "potential DDI”) or
   Potential Drugs describes the potential for one drug to affect
   another’s effects when taken concurrently. A medication has to interact
   with a matching protein or enzyme on the receptor to affect it. The
   interaction between proteins and bioactive molecules is crucial for
   many pharmacological or prophylactic approaches.

   Drug-protein interaction refers to the interaction between a protein
   molecule and a drug molecule, which significantly impacts the activity
   of medications.

   DrugBank Online is a crucial tool in the drug industry, enabling
   significant advancements in the data-driven medicine sector due to its
   comprehensive referencing and detailed data descriptions.

   The program Analyst was used to predict drug-protein correlations.

Result

Evolutionary metrics and results of statistical models

   Here, we attempt to interpret the numerous signs that might be employed
   to determine the effectiveness of the recommended approach. More
   accuracy is required when assessing models employing RNa-sequence data
   (Fig. [96]3). Even though the efficacy of a model is frequently
   evaluated using accuracy, Therefore, in addition to accuracy, the
   evaluation metrics RocAuc, Precision, Recall, Specificity, F1-Score,
   TPR, and FPR are utilized to obtain an additional thorough
   comprehension of a prototype’s effectiveness. Each of these metrics was
   used in the research we conducted to evaluate the suggested model’s
   reliability. The matrix of uncertainty compiles the several metrics
   that are employed to assess how effective a classification model is.
   The following four components are required for this confusion matrix:
   The acronyms TP, FP, TN, and FN represent the terms “True Positive,”
   “True Negative,” and “False Negative,” in that order (FN). Four things
   may happen. True positives are tallied when an incident is identified
   as positive and is deemed positive; false negatives are counted when an
   event is labeled as negative. A true negative is counted if the
   instance is classed as negative; a false positive is tallied if it is
   labeled as positive. The most frequent results are appropriate labeling
   (TP) and identification (TN), as opposed to incorrect labeling (FP and
   FN). The percentage of accurate forecasts to all predictions is known
   as accuracy. True positives (TP) and true negatives (TN) make up
   correct forecasts. The totality of the positive (P) and negative (N)
   instances make up each forecast. N is made up of false negatives (FN)
   and TN, whereas P is made up of TP and false positives (FP).
   graphic file with name d33e710.gif 1

Fig. 3.

   Fig. 3
   [97]Open in a new tab

   Evaluation among the ML model.

   When evaluating a classification model’s capacity to forecast
   tuberculosis, precision, and recall are essential metrics to consider.
   Precision is the percentage of correctly estimated victims of
   tuberculosis to all expected patients^[98]40. So the precision metric
   quantifies the proportion of accurate forecasts generated by the model
   and the amount of accurate positive predictions, or true positives,
   divided by the total number of positive predictions (including true and
   false positives) that the model correctly anticipated is the precision.

   Conversely, recall, which is often referred to as responsiveness, is
   calculated by dividing the sum of true positive labels by the sum of
   all real positive labels^[99]41. It compares the percentage of
   correctly estimated TB patients with the total number of TB patients.
   The formulas used for these metrics are:
   graphic file with name d33e735.gif 2
   graphic file with name d33e741.gif 3

   The average of the harmonics of a classification model’s recall and
   precision is known as the F1 score, or F-measure^[100]42. The F1
   measure accurately reflects a models depend- ability since both metrics
   have an equivalent role in the outcome. The equation used for the F1
   score is:
   graphic file with name d33e753.gif 4

   The classification assessment statistic for a model, called
   specificity, measures the pro-portion of true negatives that the
   framework correctly detects. This suggests that a further percentage of
   real negative data was misinterpreted as positive; One may call these
   “false positives.” The model’s elevated specificity means that most of
   the negative findings are being correctly classified by the model. In
   contrast, a low specificity indicates that many negative results are
   being incorrectly labeled as positive. Since the expense of false
   negatives is substantial like when it comes to medical treatment, high
   specificity is desired^[101]43. Specificity can be computed using the
   formula below:
   graphic file with name d33e765.gif 5

   In the context of a matrix of confusion, sensitivity or recall are
   other names for the True Positive Rate (TPR). True Negative Rate (TNR)
   is also often utilized, much like specificity. A low FPR is crucial to
   prevent needless additional testing and possible patient damage,
   whereas a high TPR is crucial to guarantee that every single cancer
   case is identified during medical treatment (Fig. [102]4). Maintaining
   the efficacy and security of tests used for diagnosis and screening for
   medical conditions requires striking a balance between TPR and FPR. It
   is computed as follows:
   graphic file with name d33e776.gif 6
   graphic file with name d33e782.gif 7

Fig. 4.

   [103]Fig. 4
   [104]Open in a new tab

   Supervised learning to diagnosis tuberculosis.

   We employed five untrained models—XG Boost, Logistic Regression, Random
   Forest Classifier, AdaBoost, and Support Vector Machine—to predict
   tuberculosis from RNA-Sequence count data. From Table [105]2 the
   results of the confusion matrices for Precision 0.95, Recall 0.964,
   RocAuc 0.985, Specificity 0.962, F1-Score 0.957, TPR 0.964, and FPR
   0.038 demonstrate that the XG Boost model worked effectively, with the
   greatest prediction accuracy at 0.963% and lowest Log Loss at 0.139%.
   Furthermore, with a prediction accuracy of 0.866%, the AdaBoost plus
   Support Vector Machine model demonstrated the second-highest accuracy.
   Their respective Log Losses are 0.666% and 0.661%, making them the
   highest. AdaBoost Precision 0.845, F1-Score 0.844, TPR 0.840, FPR
   0.114, Recall 0.840, Specificity 0.886, and RocAuc 0.060 which is the
   lowest for this specific dataset are shown in the sections that follow.
   AdaBoost’s ROC AUC value, which measures a model’s ability to
   distinguish between positive and negative classes, is the lowest.
   Moreover, for Support Vector Machine F1-Score 0.838, TPR 0.804, FPR
   0.087, Precision 0.874, Recall 0.804, RocAuc 0.115, Specificity 0.913.
   On the other hand, with an accuracy of 0.739%, Logistic Regression is
   the least accurate model overall. In addition, the Random Forest
   Classifier’s success rate in third place was 0.772%.

Table 2.

   Model efficiency assessment: evaluation metric scores.
   ML models Accuracy Log loss Precision Recall RocAuc Specificity
   F1-score TPR FPR
   XG boost 0.963 0.139 0.95 0.964 0.985 0.962 0.957 0.964 0.038
   Logistic regression 0.739 0.553 0.737 0.609 0.193 0.837 0.667 0.609
   0.163
   Random forest 0.773 0.555 0.815 0.609 0.099 0.897 0.697 0.609 0.103
   AdaBoost 0.866 0.666 0.847 0.840 0.060 0.886 0.844 0.840 0.114
   SupportVector machine 0.866 0.661 0.874 0.804 0.115 0.913 0.838 0.804
   0.087
   [106]Open in a new tab

Comparative transcription sequencing utilizing the significance of features

   To discover extremely expressed genes, Differential Gene Expressions
   (DEGs) have been performed in this study by using two
   feature-importance methodologies utilizing algorithms that use machine
   learning using count data of RNA-Sequence of TB with patients and
   non-TB. The maximum effectiveness was attained by training five
   controlled techniques: XG Boost, Logistic Regression, Random Forest
   Classifier, AdaBoost, and Support Vector Machine. XGBoosting, on the
   other hand, worked effectively, showing excellent prediction accuracy
   levels at 0.963% whereas the rest of the algorithms were below 0.9,
   which is why we selected it. The top 100 frequently occurring genes
   were then selected from the XGBoost algorithm to minimize uncertainty.
   Furthermore, the Extended data contain our expected expressed genes in
   the supplementary file Table [107]S1. Typically, P-value, Adjusted
   P-value, and Log-FC are used to identify important genes; however, we
   focused on picking out features to identify them in a new way,
   resulting in an effective result.

Assessment of gene ontology and pathway enrichment analysis

   Considering Gene Ontology (GO) provides a comprehensive description of
   protein functions, it is considered one of the essential components of
   physiological description. GO refers to a controlled and structured
   phrase set of words called GO terms^[108]44. The study usually yields
   an ordered set of GO terms having P-values corresponding to every
   phrase^[109]45. Pathway analysis is an effective method for identifying
   genes, proteins, and metabolites that function differentially and are
   generated by present high volumes screening. It is also useful in
   studying physiology^[110]46. Pathway analysis is a technique used in
   genome-wide association research or genomics tools for the preliminary
   identification and understanding of a diseased or physiological
   state^[111]47. Ontology and pathways designed to carry out a
   comprehensive physiological simulation method are essential components
   of physiological treatments. We used an expression set enhancement
   strategy to identify networks using the machine learning program
   EnrichR. Five pathway resources were used to perform tests using DEGs
   of TB. Figure [112]5 displays the 20 major parameters of the signaling
   pathways. The following Table [113]3 lists the top 10 functions related
   to cellular components, biological processes, and the top 4 for
   molecular processes. Both the GO and the Pathways are filtered by the
   adj. P-value, which is often less than 0.05. The results are then
   arranged in ascending order.

Fig. 5.

   [114]Fig. 5
   [115]Open in a new tab

   An overview of the network abundance for tuberculosis DEGs. Y axis
   signaling pathways and X-axis denoted as negative log[10] P value.
   Asthma has highest negative log[10] P value.

Table 3.

   Investigation of DEGs through an ontological standpoint.
   GO category GO ID Term P-value Genes
   Biological process GO:0,002,842 Positive regulation of T cell-mediated
   immune response to tumor cell 0.000245087 HLA-DRB3;HLA-DRB1
   GO:0,002,839 Positive regulation of immune response to tumor cell
   0.000366432 HLA-DRB3;HLA-DRB1
   GO:0,002,840 Regulation of T cell-mediated immune response to tumor
   cell 0.000366432 HLA-DRB3;HLA-DRB1
   GO:2,000,514 Regulation Of CD4-positive, Alpha–Beta T Cell Activation
   0.000511333 HLA-DRB3;HLA-DRB1
   GO:2,000,516 Positive regulation Of CD4-positive, Alpha–Beta T cell
   activation 0.001581045 HLA-DRB3;HLA-DRB1
   GO:0,002,399 MHC class II protein complex assembly 0.002165765
   HLA-DRB3;HLA-DRB1
   GO:0,002,503 Peptide antigen assembly with MHC class II protein complex
   0.002165765 HLA-DRB3;HLA-DRB1
   GO:0,002,501 Peptide antigen assembly with MHC protein complex
   0.003594237 HLA-DRB3;HLA-DRB1
   GO:0,002,381 Immunoglobulin production involved in immunoglobulin
   mediated immune response 0.004004046 HLA-DRB3;HLA-DRB1
   GO:0,046,635 Positive regulation of Alpha–Beta T cell activation
   0.004004046 HLA-DRB3;HLA-DRB1
   Cellular component GO:0,042,613 MHC class II protein complex
   0.002490834 HLA-DRB3;HLA-DRB1
   GO:0,030,662 Coated vesicle membrane 0.002635341 TEPSIN;HLA-DRB3;
   HLA-DRB1
   GO:0,042,611 MHC protein complex 0.004885387 HLA-DRB3;HLA-DRB1
   GO:0,030,134 COPII-coated ER to golgi transport vesicle 0.006547998
   TMED3;HLA-DRB3; HLA-DRB1
   GO:0,098,553 Lumenal side of endoplasmic reticulum membrane 0.008008107
   HLA-DRB3;HLA-DRB1
   GO:0,032,588 Trans-golgi network membrane 0.013821758 TEPSIN;HLA-DRB3;
   HLA-DRB1
   GO:0,012,507 ER to golgi transport vesicle membrane 0.030955668
   HLA-DRB3;HLA-DRB1
   GO:0,030,658 Transport vesicle membrane 0.036310368 HLA-DRB3;HLA-DRB1
   GO:0,030,669 Clathrin-coated endocytic vesicle membrane 0.045552255
   HLA-DRB3;HLA-DRB1
   GO:0,030,666 Endocytic vesicle membrane 0.045588079 HVCN1;HLA-DRB3;
   HLA-DRB1
   Molecular function GO:0,032,395 MHC class II receptor activity
   0.000870867 HLA-DRB3;HLA-DRB1
   GO:0,023,026 MHC class II protein complex binding 0.006889074
   HLA-DRB3;HLA-DRB1
   GO:0,042,609 CD4 receptor binding 0.034484249 HLA-DRB1
   GO:0,005,388 P-type calcium transporter activity 0.039313504 ATP2B2
   [116]Open in a new tab

Protein–protein interactions (PPIs) analysis and hub genes identification

   The capacity of compounds to operate as drugs and the target protein’s
   activity are largely determined by protein-protein interactions. Most
   proteins and genes recognize the activities of the ensuing phenotype as
   a collection of interconnections. Cell-to-cell contacts, regulation of
   metabolism, evolutionary supervision, and other functions in biology
   are all managed by protein-protein interactions or PPIs^[117]48. The
   PPI network was analyzed using STRING, and compliance networks and
   recurring connections among DEGs were predicted using a Cytoscape
   visualization. By using topological measures, such as a degree greater
   than 15°, PPI analysis was used to designate highly communicative
   proteins. The PPI network (Fig. [118]6) has 40 nodes and 76 edges
   connecting them, which are the most notable DEGs. Hub genes exhibit the
   top 10% interconnectedness and a significant correlation with potential
   units. Because of these interactions, hub genes usually have a major
   function in biological systems. We utilized the Cytohubba plugin in
   Cytoscape to identify the top 20 DEGs or hub genes. Notably, Fig.
   [119]7 depicts the hub genes identified using the MCC approach: CREBBP,
   SPR, H2AX, CD84, LILRB2, UTY, TFEB, LILRB1, FOXI2,  and HVCN1, while
   the Bottleneck approach identified LILRB2, CD84, LILRB4, HLA-DPB1,
   HLA-DQB1, HLA-DRB1, LILRB1, C1QB, CD160, and CREBBP as hub genes.

Fig. 6.

   [120]Fig. 6
   [121]Open in a new tab

   PPI network is made up of DEGs for tuberculosis. Differentially ex-
   pressed protein genes are represented by the circular nodes in the
   picture, and the interaction between the nodes is shown by the edges.
   The PPI is made up of 40 nodes and 76 edges. STRING was used to build
   the PPI network, and Cytoscape was used to view it.

Fig. 7.

   [122]Fig. 7
   [123]Open in a new tab

   Identification of hub genes within the cluster using cytohubba:
   application of MCC (Maximal Clique Centrality) and bottleneck
   algorithms and network comparison. The linkages between the top 10 hub
   genes from each method and additional genes (yellow) are indicated by
   dark green high- lights. While the (A) BottleNeck has 30 nodes and 65
   edges (B) MCC network has 22 nodes and 55 edges.

Discovering the miRNAs and transcription factors (TF) that bind to their
neighboring DEGs

   We employed a system that uses a network approach to analyze the
   controlling TFs and miRNAs to identify significant expression
   alterations and discover additional signaling molecules associated with
   the hub protein. Proteins known as transcription factors are substances
   that control transcription as well as gene activity in all living
   organisms^[124]49. MiRNAs, which are minuscule RNA molecules, are
   involved in the modulation of post-transcriptional expression. Figure
   [125]8 depicts the interaction between DEGs and TFs, while Fig. [126]9
   shows the relationship between DEGs and miRNAs. TFs of genes with
   differential expression have major regulators that were STAT3, GATA2,
   KLF4, MYC, FLI1, TP53, REST, HNF4A, FOXP1, POU5F1, SPI1, NANOG, SOX2,
   PPARG, CREM, GATA2, NFKB1, E2F1, JUN, USF2, PPARG, HOXA5, FOXC1, and
   YY1 (Table [127]4). has-mir-383-3p, has-mir-520h, has-mir-520g-3p,
   has-mir-7977, has-mir-218-5p, has-mir-6499- 3p, has-mir-30a-5p,
   has-let-7b-5p, has-mir-26b-5p, has-mir-27a-3p, has-mir-129-2-3p,
   has-mir-34a-5p, has-mir-1-3p, has-mir-16-5p, has-mir-124-3p, and
   has-let-7b-5p were defined to create a succinct summary of the DEGs
   acting as post-transcriptional regulators. The transcriptional and
   post-transcriptional regulatory elements of the genes asso ciated with
   TB that are differently regulated are compiled in Tables [128]4 and
   [129]5 respectively.

Fig. 8.

   [130]Fig. 8
   [131]Open in a new tab

   The Network Analyst’s framework for integrated regulated collaboration
   among DEGs and TFs, using (a) ChEA and (b) Jasper database. (a) The
   network contains 47 nodes and 196 edges, where (b) has 31 and 95, nodes
   and edges respectively. Transcription factors are represented by square
   nodes, while genes that are connected to transcription factors are
   represented by circular nodes.

Fig. 9.

   [132]Fig. 9
   [133]Open in a new tab

   The interconnectedness of regulated relationships between miRNAs and
   DEGs. Here, the circular gene representations link to the miRNAs, which
   are represented by the square node. Network (a) contains 22 nodes and
   29 edges while (b) has 23 nodes and 54 edges both are constructed using
   miRTarBase and TarBase databases respectively.

Table 4.

   Overview of transcriptional factor (TF) biomolecules of differentially
   expressed genes of tuberculosis.
   TF name Description Function
   FOXC1 Forkhead box C1 Transcription factor activity that binds DNA and
   transcription factor binding
   HOXA5 Homeobox A5 Transcription factor activity that binds DNA and RNA
   polymerase II
   PPARG Peroxisome proliferator-activated receptor gamma Transcription
   factor activity that binds DNA and chromatin linking
   USF2 Upstream transcription Factor 2, C-Fos interacting Transcription
   factor activity that binds DNA and sequence-specific DNA binding
   JUN Jun proto-oncogene, AP-1 transcription factor subunit RNA binding
   and sequence-specific DNA binding
   E2F1 E2F transcription factor 1 Transcription factor activity that
   binds DNA and transcription factor binding
   NFKB1 Nuclear factor kappa B subunit 1 Transcription factor activity
   that binds DNA and sequence-specific DNA binding
   GATA2 GATA binding protein 2 Transcription factor activity that binds
   DNA and chromatin binding
   YY1 YY1 transcription factor Transcription factor activity that binds
   DNA and transcription coactivator activity
   RUNX1 RUNX family transcription factor 1 Transcription factor activity
   that binds DNA and protein homodimerization activity
   FOXP1 Forkhead box P1 Transcription factor activity that binds DNA and
   sequence-specific DNA binding
   HNF4A Hepatocyte nuclear factor 4 alpha Transcription factor activity
   that binds DNA and sequence-specific DNA binding
   REST RE1 silencing transcription factor Transcription factor activity
   that binds DNA and transcription factor binding
   TP53 Tumor protein P53 Transcription factor activity that binds DNA and
   protein heterodimerization activity
   FLI1 Fli-1 proto-oncogene, ETS transcription factor Transcription
   factor activity that binds DNA and chromatin binding
   MYC MYC proto-oncogene, BHLH transcription factor Transcription factor
   activity that binds DNA and RNA polymerase II cis-regulatory region
   sequence-specific DNA binding
   KLF4 KLF transcription factor 4 Transcription factor activity that
   binds DNA and transcription factor binding
   STAT3 Signal transducer and activator of transcription 3 Transcription
   factor activity that binds DNA and sequence-specific DNA binding
   CREM CAMP responsive element modulator Transcription factor activity
   that binds DNA and core promoter sequence-specific DNA binding
   SOX2 SRY-Box transcription factor 2 Transcription factor activity that
   binds DNA and protein heterodimerization activity
   NANOG Nanog homeobox Transcription factor activity that binds DNA and
   chromatin binding
   SPI1 Spi-1 proto-oncogene Transcription factor activity that binds DNA
   and RNA binding
   POU5F1 POU class 5 homeobox 1 RNA binding and DNA binding particular to
   certain sequences
   [134]Open in a new tab

Table 5.

   Overview of miRNA biomolecules of differentially expressed genes of
   tuberculosis.
   Name Description Function
   hsa-mir-383-3p MicroRNA 383 Inhibitor of tumors in several different
   cancer types, such as thyroid, kidney, and cervical cancer^[135]1
   hsa-mir-520 h MicroRNA 520 h Downregulated Death-associated protein
   kinase 2 (DAPK2) expression^[136]2 , and decrease of side population
   cells and prevention of cell propagation and invasion in PANC1 cell
   line^[137]3
   hsa-mir-520 g-3p MicroRNA 520 g Associated with reduced procoagulant
   and signaling potential of cancer cells^[138]4
   hsa-mir-7977 MicroRNA 7977 Novel indicator to patients of lung
   adenocarcinoma and may play as tumor suppressor in lung cancer^[139]5
   hsa-mir-218-5p MicroRNA 218–1 Has a vital function in preventing lung
   cancer cells from proliferating and migrating, most likely by attaching
   to the EGFR receptor^[140]6
   hsa-mir-6499-3p MicroRNA 6499 3p MiR-6499-3p was predicted to target
   the NFKBIA gene. NFKBIA has a role in the acute stage responsive
   signaling, which includes T and neutrophil activation^[141]7
   hsa-mir-30a-5p MicroRNA 30a Increases paclitaxel (lung cancer cell)
   sensitivity by suppressing BCL-2, a crucial apoptosis regulator, thus
   encouraging chemotherapy-induced apoptosis^[142]8
   hsa-let-7b-5p MicroRNA Let-7b Prohibits the Transportation and
   poliferation of growth factor derived from platelets (PDGF) triggered
   Pulmonary Artery Smooth Muscle Cells (PASMCs) by modulating IGF1^[143]9
   hsa-mir-26b-5p MicroRNA 26b Enhanced apoptosis, decreased fibroblasts,
   and reduced lung fibrosis in mice bronchial tissues, and decreased
   mortality in mouse bronchial tissues due to upregulation of
   miR-26a-5p^[144]10
   hsa-mir-27a-3p MicroRNA 27a Lung fibroblast development into
   myofibroblasts was boosted by miR-27a-3p suppression, while
   overexpression was hindered^[145]11
   hsa-mir-129–2-3p MicroRNA 129–2 Members of the MiR-129 family are
   widely acknowledged to be tumor suppressors, exhibiting reduced
   expression in several types of cancers^[146]12
   hsa-mir-34a-5p MicroRNA 34a In the p53 wild-type tumor in the colon
   cell HCT116, miR-34a-5p markedly reduced cell proliferation, migration,
   invasion, and metastasis^[147]13
   hsa-mir-1-3p MicroRNA 1–1 Had a restraint effect on the tumorigenic
   potential of lung adenocarcinoma (LUAD) cells, as demonstrated by the
   markedly decreased vitality, movement, and incursion of LUAD cells upon
   miR-1-3p upregulation. Furthermore, miR-1-3p showed conspicuous
   downregulation in human cell lines and LUAD tissues^[148]14
   hsa-mir-16-5p MicroRNA 16–1 Since miR-16-5p is not highly expressed in
   breast cancer tissues, it can prevent breast cancer by inhibiting the
   NF-κ B pathway and raising the level of AKT3^[149]15
   hsa-mir-124-3p MicroRNA 124–3 miR-124 expression at a low level in
   breast cancer tissue was linked to poor prognosis of patients^[150]16
   hsa-let-7b-5p MicroRNA Let-7b hsa-let-7b-5p may prevent glioma cells
   from migrating, invading, and going through cell cycle^[151]17
   [152]Open in a new tab

Potential medication

   To comprehend the molecular components associated with the transmission
   of signals, a protein-drug interaction study must be carried
   out^[153]50. Using NetworkAnalyst approaches based on drug-protein
   interactions from the DrugBank library, we identified 22 prospective
   therapy medications for frequently occurring DEGs as promising
   medicinal options in TB. Figure [154]10 displays 22 widely used
   medicinal substances, Bevacizumab, Daclizumab, Palivizumab,
   Natalizumab, Efalizumab, Alefacept, Alemtuzumab, Tositumomab,
   Ibritumomab tiuxetan, Muromonab, Basiliximab, Rituximab, Trastuzumab,
   Gem- tuzumab ozogamicin, Abciximab, Adalimumab, Etanercept, Cetuximab,
   N-Acetyle Sero- tonin, Biopterin, and 2’-Monophosphoadenosine
   5’-Diphosphoribose which had been identified in the DEGs of TB Protein
   Drug Associations.

Fig. 10.

   [155]Fig. 10
   [156]Open in a new tab

   This figure depicted 22 potential medications for tuberculosis
   treatment identified through the protein-drug interaction approach.
   Among them, 18 drugs target the C1QB gene, while the others interact
   with the SPR gene. In the diagram, medications are represented by
   rectangular nodes, and their corresponding gene targets are depicted as
   spherical symbols.

Discussion

   Nowadays, machine learning is becoming increasingly important in the
   bioinformatics industry as artificial intelligence advances quickly,
   allowing for in-depth data analysis. While most RNA-Sequence data may
   be processed using machine learning approaches, tuberculosis dataset
   has been examined in this study since it is a infectious disease with
   potentially fatal consequences. In the investigation, we introduced a
   count- oriented classification workflow for RNA sequence information
   analysis that uses well- established machine learning techniques to
   effectively detect tuberculosis. Additionally, the methods employed
   here allow us to deal with substantial transcriptome data and make
   trustworthy inferences about TB proteins. Notably, we made an effort to
   create a novel approach by using the machine learning technique’s key
   characteristic technique to the TB data in order to identify genes with
   enhanced expression of RNA-Sequence data. Also, we employed a variety
   of bioinformatics tools in our investigation, which enables us to fully
   comprehend tuberculosis and pinpoint its related biomarkers. We used
   multiple trained machine learning methods, such as Random Forest,
   AdaBoost, Gradient Boosting, Logistic Regression, Decision Tree, and
   XGBoost, in our detailed examination of tuberculosis (TB). Figure
   [157]3 showed their accuracy, losses, and performance indicators in
   graphic style. The Table [158]2 provides more information on each of
   these indicators. we investigated Pathway enrichment assessments in
   Fig. [159]5, Gene Ontology analysis in Table [160]3, Protein-Protein
   Interaction examinations in Fig. [161]6, and investigated Hub-Protein
   interactions in Fig. [162]7, Transcriptional Factor interactions in
   Fig. [163]8, miRNA interactions in Fig. [164]9, and drug-protein
   interactions in Fig. [165]10.

   There are various reasons why our top model, XGBoost, performed better
   on our dataset than the others. With integrated L1 and L2
   regularization and the capacity to represent intricate non-linear
   relationships, it excelled in managing high-dimensional data. Its
   efficiency is further increased by special features including tree
   pruning, effective column block computation, and internal handling of
   missing values. The model’s edge was probably influenced by its
   flexibility in adjusting hyperparameters, resistance to outliers, and
   capacity to record features engagement. Furthermore, some datasets’
   intrinsic characteristics may be more compatible with gradient-boosted
   trees, indicating that the underneath patterns in our data were
   especially suitable to XGBoost. Below is the mathematical formula for
   XGBoost model’s objective and procedure: The model’s prediction for the
   i^th instance at the t^th iteration of a dataset comprising n samples
   and m characteristics is represented by the symbol yˆ[i]^t). In every
   iteration of XGBoost, the following objective function needs to be
   optimized:
   graphic file with name d33e1804.gif 8

   When the regularization term, Ω, is defined as follows and l is the
   loss function with differentiable convexity:
   graphic file with name d33e1812.gif 9

   In this case, w[j] is the score given to the j^th leaf, and T shows the
   number of leaves in the tree. The tree’s ultimate configuration is
   determined by minimizing:
   graphic file with name d33e1830.gif 10

   where g, and h denote the corresponding sum of the cases that reach the
   child node on the left, G, and H stand for the total of the loss
   function’s first- and second-order gradients for the instances in the
   current node.

   In bioinformatics, gene ontology (GO) and enrichment analysis are
   widely utilized statistical methods that help researchers uncover the
   biological significance of large gene sets. GO suggests that biological
   processes can involve molecular interactions. The molecular aspect
   focuses on molecular processes, while the cellular component refers to
   the location where a gene exerts its function. Meanwhile, pathway
   analysis offers a distinct approach by exploring the connections
   between physiologically or molecularly complex diseases. This is the
   most efficient way to make an organism react to changes occurring
   inside of it. From Table [166]3 the leading 10 genes of biological
   process, cellular component, and molecular function are mentioned in
   turn according to their P values. The most significant genes are
   HLA-DRB3; and HLA-DRB1 for these above-mentioned three processes.
   HLA-DRB1 alleles could represent an increased risk of developing active
   TB^[167]51. Other important DEGs are TEPSIN, TMED3, HVCN1, and ATP2B2
   for these three categories. Figure [168]5 showed the pathway enrichment
   summary of tuberculosis DEGs. According to the figure, the top pathways
   are asthma, allograft rejection, Graft-versus-host disease, and Type I
   diabetes mellitus.

   In our study, the protein-protein interaction (PPI) network was
   analyzed using Cytoscape and STRING. Our method combined the robust
   display and analysis capabilities of Cytoscape with the huge PPI
   database of STRING. With the use of Cytoscape’s sophisticated network
   analysis tools and STRING’s carefully selected data, this integration
   enables a thorough investigation of the interaction network, resulting
   in a more in-depth and understandable knowledge of protein
   interactions. This, in our opinion, makes our method a useful addition
   to already-existing computational techniques, especially in situations
   where computational resources or data availability may be restricted.
   In contrast to certain machine learning techniques that could need a
   lot of training data and feature building, our methodology uses
   Cytoscape’s network measurements and STRING’s preset interaction scores
   directly to identify key proteins. In conclusion, our suggested
   workflow presents a well-balanced strategy for combining the
   interactive features of Cytoscape with reliable data from STRING,
   making it easy to use and understand for PPI network analysis.

   Hub proteins of high or low expression levels in TB patients have been
   found. TB quickly triggers the transcription factor cAMP Response
   Element Binding Protein (CREBP), which controls a variety of cellular
   reactions in macrophages^[169]52. Experiments on the M. tuberculosis
   challenge utilizing CD84-deficient C57BL/6 mice indicate that CD84
   expression probably contributes to immunosuppression of T and B cells
   during M. tuberculosis pathogenesis and also inhibits B cell
   activation^[170]53. Myeloid-derived suppressor cells (MDSCs) are
   immunosuppressive cells that are reprogrammed to become
   pro-inflammatory by LILRB2 antagonists, which kills Mtb^[171]54.
   Compared to healthy controls, patients with active tuberculosis had
   reduced CD160 expression in their B cells and monocytes^[172]55.

   The TFs we found are connected to TB. Table [173]5 shows the overview
   of TF biomolecules of differentially expressed genes. NFKB1, one of the
   most influential hub genes was determined as a potential therapeutic
   target in tuberculosis^[174]56. GATA2 is downregulated by sustained
   stimulation with M. tuberculosis antigen^[175]57. After M. tuberculosis
   infection, TLR4 which is YY1-activated may exacerbate mycobacterial
   damage in human microbes^[176]58.

   Tuberculosis is also associated with our discovered miRNAs. Serum
   exosomes may be a useful marker for exosomal miR-7977, which is crucial
   for lung cancer diagnosis^[177]59. In both MTB-infected and uninfected
   THP-1 cells, forced overexpression of miR- 30a had a restraining action
   on TLR/MyD88 activation and cytokine production^[178]60. mir- 26b-5p
   might function as biomarker for IOTB(Intraocular Tuberculosis)^[179]61.

   We want to delineate the particular benefits of utilizing machine
   learning strategies like AdaBoost, XGBoost, and Logistic Regression
   classifiers in comparison to more conventional approaches like R-based
   statistical methodologies and other well-established bioinformatics
   tools. Multiple weak learners are used by machine learning models,
   especially ensemble techniques like AdaBoost and XGBoost, to create a
   strong prediction model. When compared to single statistical methods,
   this approach frequently yields better generalization and higher
   accuracy. ML classifiers were able to identify complicated, non-linear
   correlations in the data, which improved the accuracy of identifying
   genes that were differentially expressed in differential gene analysis.
   Gene expression profiles, genetic variants, and route information are
   just a few of the data types that our machine-learning algorithms
   effectively manage and integrate. Even if they are strong, traditional
   approaches could have trouble integrating diverse data sources. With
   their capacity to process and learn from intricate, multi-dimensional
   datasets, machine learning techniques offer a more comprehensive
   perspective and reveal interactions that traditional tools would
   overlook. Because machine learning algorithms are flexible, they can
   find new patterns and connections in the data that might be missed by
   conventional tools. They can find new patterns and connections in the
   data. For instance, when we used XGBoost to identify hub genes, we were
   able to identify possible important regulators that were previously
   missed by traditional centrality metrics. Large-scale genomic
   investigations can benefit from our ML algorithms’ better computing
   efficiency and quicker processing times as compared to other
   conventional methods.

   For TB patients, our identified potential therapeutic compounds may be
   beneficial. Treatment with bevacizumab reduces hypoxia in TB
   granulomas, enhances small molecule delivery, and encourages vascular
   normalization^[180]62. There is some ambiguity on how daclizumab,
   palivizumab, natalizumab, efalizumab, tositumomab, alefacept,
   muromonab, and others react with TB. Thus, preclinical and clinical
   trials together with additional research are needed.

Conclusion

   Our study endeavors to employ a machine learning method to detect
   important genes using the feature significance tools and to confront
   the overarching global challenge of tuberculosis and advance the
   well-being and livelihoods of individuals. Here, we explored both
   machine learning techniques and conventional bioinformatics methods.

   We now have a more comprehensive understanding of the molecular
   pathways behind tuberculosis sickness because of the integration of
   machine learning algorithms, statistical analysis, and bioinformatics
   tools. This approach would allow us to more effectively identify
   expressed genes, moving beyond reliance only on conventional
   bioinformatics methods with a specific focus on detecting DEGs. In
   addition, when dealing with a massive amount of features, employing a
   machine learning approach proves to be effective as well as more
   reliable in predicting DEGs. We’ve analyzed vast transcriptomic data
   successfully using multiple ML models. XGBoost model demonstrating
   strong performance achieved 96.3% accuracy. This shows that ML can
   enhance TB diagnosis, enabling tailored treatments and revolutionizing
   clinical practice.

   In our TB biomarker study, we analyzed various molecular components,
   such as 20 pathways, 24 gene ontologies, 23 transcription factors, 20
   hub genes, 16 miRNAs, and 22 possible drugs. Based on our verification,
   we validated our study. The three main pathways are hematopoietic cell
   lineage, Th1 and Th2 cell development, and antigen processing, as well
   as presentation Fig. [181]5. The reported Hub proteins such as CD84,
   LILRB2, LILRB4, and CD160 might provide encouraging pathways for
   advancing disease study and therapy in Table [182]6. Similarly,
   transcriptional factors like FOXC1, GATA2, YY1, SPI1, MYC, and SOX2 in
   Fig. [183]8, coupled with miRNAs like has-mir-7977, hsa-let-7b-5p,
   hsa-mir-26b-5p, hsa-mir-16-5p, hsa-mir-124-3p, and hsa-mir-1-3p in Fig.
   [184]9, explore the regulatory dynamics at the core of TB. Moreover,
   our study pinpointed prospective drug candidates such as biopetrin,
   oxaloacetate ion, bevacizumab, and other compounds, showing potential
   for therapeutic application in Fig. [185]10. Although more preclinical
   and clinical research is required to confirm their safety and efficacy
   profiles, these discoveries present opportunities to transform TB
   therapy approaches and enhance patient outcomes. This research marks a
   notable step forward in utilizing machine learning for detecting genes
   that are expressed from RNA sequence count data. By combining essential
   bioinformatics techniques and verifying results opposing previous
   studies on TB, we provide a strong method for comprehending and
   tackling TB on a molecular level.

Table 6.

   Analysis of protein–protein interactions identifies hub genes that DEGs
   have accumulated.
   Gene Symbol Description Feature
   CREBBP CREB binding protein DNA-binding transcription factor activity
   and transcription factor binding
   SPR Sepiapterin reductase Oxidoreductase activity and aldo–keto
   reductase (NADP) activity
   H2AX H2A.X variant histone Play a central role in transcription
   regulation, DNA repair, DNA replication, and chromosomal stability
   CD84 CD84 molecule Signaling receptor activity
   LILRB2 Leukocyte immunoglobulin like receptor B2 Signaling receptor
   activity and protein phosphatase 1 binding
   UTY Ubiquitously transcribed tetratricopeptide repeat containing,
   Y-Linked Dioxygenase activity and histone H3K27me2/H3K27me3 demethylase
   activity
   TFEB Transcription factor EB DNA-binding transcription factor activity
   and protein dimerization activity
   LILRB1 Leukocyte immunoglobulin like receptor B1 Protein
   homodimerization activity and protein phosphatase 1 binding
   FOXI2 Forkhead box I2 DNA-binding transcription factor activity and
   DNA-binding transcription factor activity, RNA polymerase II-specific
   HVCN1 Hydrogen voltage-gated channel 1 Monoatomic ion channel activity
   and voltage-gated proton channel activity
   CD84 CD84 molecule Signaling receptor activity
   LILRB4 Leukocyte immunoglobulin like receptor B4 Signaling receptor
   activity and antigen binding
   HLA-DPB1 Major histocompatibility complex, class II, DP beta 1 Peptide
   antigen binding
   HLA-DQB1 Major histocompatibility complex, class II, DQ beta 1 Peptide
   antigen binding and MHC class II receptor activity
   HLA-DRB1 Major histocompatibility complex, class II, DR beta 1 Peptide
   antigen binding and MHC class II receptor activity
   C1QB Complement C1q B chain Protein homodimerization activity
   CD160 CD160 molecule Signaling receptor binding and MHC class I
   receptor activity
   [186]Open in a new tab

Limitations of the studying

   A limitation of this research is the requirement for improved precision
   for the models. Although our present methodology has yielded some
   insightful findings, the incorporation of machine learning models in
   further research endeavors may considerably augment the precision and
   accuracy of our results. Furthermore, we still need to use the evidence
   of research currently available for validating a subset of biomarkers.
   Thoroughly validating those biomarkers is crucial, as it may provide
   researchers with a better understanding of expressed genes, eventually
   allowing more accurate analysis and enhanced TB management tactics

Supplementary Information

   [187]Supplementary Information.^ (15.8KB, docx)

Acknowledgements