Abstract Background In this study, we explored the gene prioritization in preeclampsia, combining co-expression network analysis and genetic algorithms optimization approaches. We analysed five public projects obtaining 1,146 significant genes after cross-platform and processing of 81 and 149 microarrays in preeclamptic and normal conditions, respectively. Methods After co-expression network construction, modular and node analysis were performed using several approaches. Moreover, genetic algorithms were also applied in combination with the nearest neighbour and discriminant analysis classification methods. Results Significant differences were found in the genes connectivity distribution, both in normal and preeclampsia conditions pointing to the need and importance of examining connectivity alongside expression for prioritization. We discuss the global as well as intra-modular connectivity for hubs detection and also the utility of genetic algorithms in combination with the network information. FLT1, LEP, INHA and ENG genes were identified according to the literature, however, we also found other genes as FLNB, INHBA, NDRG1 and LYN highly significant but underexplored during normal pregnancy or preeclampsia. Conclusions Weighted genes co-expression network analysis reveals a similar distribution along the modules detected both in normal and preeclampsia conditions. However, major differences were obtained by analysing the nodes connectivity. All models obtained by genetic algorithm procedures were consistent with a correct classification, higher than 90%, restricting to 30 variables in both classification methods applied. Combining the two methods we identified well known genes related to preeclampsia, but also lead us to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which may have to be validated experimentally. Background Preeclampsia remains a leading cause of maternal/fetal mortality and morbidity associated with gestational hypertension and proteinuria. The underlying mechanism and preventive treatment [[28]1,[29]2] remain unknown and therefore, it is still known as the “disease of theories” [[30]3]. Due to possible multifactorial causes involved [[31]1,[32]2,[33]4], an increase in “omics” experimental approaches is noted, generating a large amount of information, not always integrated or analysed by recent methodologies. Some bioinformatics analysis were performed on specific microarray assays [[34]5-[35]7], and our group has recently carried out an extensive review of related data, processing multiple microarrays combined with text mining tools that led to the identification of several specific genes [[36]8]. In this work, we present a different strategy focused on gene prioritization by co-expression network analysis and genetic algorithms optimization. We also increase the number of microarrays processed. Methods Microarray processing Experimental microarray data comparing normal (N) and preeclamptic pregnancies (PRE) was obtained analysing the Gene Expression Omnibus (GEO) and ArrayExpress databases [[37]9,[38]10]. Only the studies comprising more than 10 subjects (by groups) were included (Table [39]1). Table 1. General microarrays information Code Database Sample Method Tissue Ref. E-TABM-682 __________________________________________________________________ Array express __________________________________________________________________ 13(PRE), 58(N) __________________________________________________________________ Illumina __________________________________________________________________ Placenta __________________________________________________________________ [[40]11] __________________________________________________________________ E-MEXP-1050 __________________________________________________________________ Array express __________________________________________________________________ 16(PRE), 17(N) __________________________________________________________________ Affymetrix __________________________________________________________________ Placenta __________________________________________________________________ [[41]12] __________________________________________________________________ [42]GSE25906 __________________________________________________________________ GEO __________________________________________________________________ 23(PRE), 37(N) __________________________________________________________________ Illumina __________________________________________________________________ Placenta __________________________________________________________________ [[43]13] __________________________________________________________________ [44]GSE14722^2 __________________________________________________________________ GEO __________________________________________________________________ 12(PRE), 11(N) __________________________________________________________________ Affymetrix __________________________________________________________________ Placenta __________________________________________________________________ [[45]14] __________________________________________________________________ [46]GSE10588 GEO 17(PRE), 26(N) ABI Human Placenta [[47]7] [48]Open in a new tab Notes: All samples were collected for biopsy of placenta during childbirth. 2) In the GEO appear two platforms ([49]GPL96, [50]GPL97) but only [51]GPL96 was used because a greater number of probes are shared with other platforms. Table [52]1 shows the GEO and Array Express data sources, references,