Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes

Marco Frasca, Dario Malchiodi

GCB, Vol. 3, No. 3 (2017): e47


Abstract


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.

Keywords


disease-gene prioritization; graph-based node ranking; cost-sensitive learning; negative selection

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DOI: http://dx.doi.org/10.18547/gcb.2017.vol3.iss3.e47

Copyright (c) 2017 Marco Frasca, Dario Malchiodi

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