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.
Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes / M. Frasca, D. Malchiodi. - In: GENOMICS AND COMPUTATIONAL BIOLOGY. - ISSN 2365-7154. - 3:3(2017).
Titolo: | Exploiting Negative Sample Selection for Prioritizing Candidate Disease Genes |
Autori: | FRASCA, MARCO (Primo) MALCHIODI, DARIO (Ultimo) |
Parole Chiave: | disease-gene prioritization; graph-based node ranking; cost-sensitive learning; negative selection |
Settore Scientifico Disciplinare: | Settore INF/01 - Informatica |
Data di pubblicazione: | 2017 |
Rivista: | |
Tipologia: | Article (author) |
Digital Object Identifier (DOI): | http://dx.doi.org/10.18547/gcb.2017.vol3.iss3.e47 |
Appare nelle tipologie: | 01 - Articolo su periodico |
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