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|
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|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.18547/gcb.2017.vol3.iss3.e47|
|Appare nelle tipologie:||01 - Articolo su periodico|