Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes


A major challenge in bio-medicine is finding the genetic causes of human diseases, and researchers are often faced with a large number of candidate genes. Gene prioritization methods provide a valuable support in guiding researchers to detect reliable candidate causative-genes for a disease under study. Indeed, such methods rank genes according to their association with a disease of interest. Actually, the majority of genetic disorders has few or none causative genes associated with them; this induces a high labeling unbalance in the corresponding ranking problems, thus linking the need of achieving reliable solutions to the adoption of imbalance-aware techniques. We propose the use of an expressly designed imbalance-aware methodology for prioritizing genes, which first rebalances the training set entries through a negative selection procedure, then applies a learning algorithm 'sensitive' to the misclassification of positive instances, to provide the gene ranking. The algorithm has a reduced time complexity, which makes feasible its application on large-sized datasets. The validation of this methodology proved its competitiveness with state-of-art techniques on a benchmark composed of 708 selected Medical Subject Headings diseases, and provided some putative novel gene-disease associations.

Author Biography

Marco Frasca, Computer Science Department, University of Milan

AnacletoLab, Computer Science Department

How to Cite
FRASCA, Marco; MALCHIODI, Dario. Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes. Genomics and Computational Biology, [S.l.], v. 3, n. 3, p. e47, may 2017. ISSN 2365-7154. Available at: <https://genomicscomputbiol.org/ojs3/GCB/article/view/5>. Date accessed: 16 jan. 2018. doi: https://doi.org/10.18547/gcb.2017.vol3.iss3.e47.
Research Articles