Gene regulatory networks define the interactions between DNA products and other substances in cells. Increasing knowledge of these networks improves the level of detail with which the processes that trigger different diseases are described and fosters the development of new therapeutic targets. These networks are usually represented by graphs, and the primary sources for their correct construction are usually time series from differential expression data. The inference of networks from this data type has been approached differently in the literature. Mostly, computational learning techniques have been implemented, which have finally shown some specialization in specific datasets. For this reason, the need arises to create new and more robust strategies for reaching a consensus based on previous results to gain a particular capacity for generalization. This paper presents GENECI (GEne NEtwork Consensus Inference), an evolutionary machine learning approach that acts as an organizer for constructing ensembles to process the results of the main inference techniques reported in the literature and to optimize the consensus network derived from them, according to their confidence levels and topological characteristics. After its design, the proposal was confronted with datasets collected from academic benchmarks (DREAM challenges and IRMA network) to quantify its accuracy. Subsequently, it was applied to a real-world biological network of melanoma patients whose results could be contrasted with medical research collected in the literature. Finally, it has been proved that its ability to optimize the consensus of several networks leads to outstanding robustness and accuracy, gaining a certain generalization capacity after facing the inference of multiple datasets. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a python package available at PyPI: https://pypi.org/project/geneci/.

Transcriptomic approaches are increasingly used in reproductive medicine to identify candidate endometrial biomarkers. However, it is known that endometrial progression in the molecular biology of the menstrual cycle is a main factor that could affect the discovery of disorder-related genes. Therefore, the aim of this study was to systematically review current practices for considering the menstrual cycle effect and to demonstrate its bias in the identification of potential biomarkers. From the 35 studies meeting the criteria, 31.43% did not register the menstrual cycle phase. We analysed the menstrual cycle effect in 11 papers (including 12 studies) from Gene Expression Omnibus: three evaluating endometriosis, two evaluating recurrent implantation failure, one evaluating recurrent pregnancy loss, one evaluating uterine fibroids and five control studies, which collected endometrial samples throughout menstrual cycle. An average of 44.2% more genes were identified after removing menstrual cycle bias using linear models. This effect was observed even if studies were balanced in the proportion of samples collected at different endometrial stages or only in the mid-secretory phase. Our bias correction method increased the statistical power by retrieving more candidate genes than per-phase independent analyses. Thanks to this practice, we discovered 544 novel candidate genes for eutopic endometriosis, 158 genes for ectopic ovarian endometriosis and 27 genes for recurrent implantation failure. In conclusion, we demonstrate that menstrual cycle progression masks molecular biomarkers, provides new guidelines to unmask them and proposes a new classification that distinguishes between biomarkers of disorder or/and menstrual cycle progression.

The development of genome-wide gene expression profiling technologies over the past two decades has produced great opportunity for researchers to explore the transcriptome and to better understand biological systems and their perturbation. In this chapter we provide an overview of microarray and massively parallel sequencing technologies and their application to gene expression analysis. We discuss factors that impact expression data generation and analysis that which should be considered in the application of these technology platforms. We further present the results of a simple illustration study to highlight performance similarities and differences in expression profiling of protein-coding mRNAs with each platform. Based on technical and analytical differences between the two platforms, reports in the literature comparing arrays and RNA-Seq for gene expression, and our own example study and experience, we provide recommendations for platform selection for gene expression studies.