Magdalena Navarro

Study of two gene expression databases obtained from the NCBI GEO database with different analytical tools to find underlying structures that provide insights into the genetic variations of Autism Spectrum Disorders and ageing. Groups of relevant genes were identified using differential expression analysis as well as module detection in correlation networks, the results were interpreted using enrichment analysis and tested for relevance by contrasting them to known human genes associated to autism from the Simons Foundation Autism Research Initiative Gene database (SFARI Gene). Although significant results were found, some deficiencies were discovered when using these approaches, mainly related to instability problems in the results, advising a meticulous approach to the way experiments are designed and analysis executed to ensure the robustness of the outcomes.

This review presents an overview from the Machine Learning perspective of the problem of identifying disease-related genes, examining some state-of-the-art methods, comparing their strengths and weaknesses, and focusing on the Positive-Unlabelled Learning for Disease Identification (PUDI) algorithm, studying the origins of the PU Learning branch of Machine Learning, some of its most popular algorithms and its incursion into the Bioinformatics field.