Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.

ABSTRACT: BACKGROUND:Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE). RESULTS:We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was cri

SUBMITTER: Arabfard M 

PROVIDER: S-EPMC6842548 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature