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

Abstract

BackgroundMachine 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).ResultsWe 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 critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage).ConclusionWe predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.

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Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

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The Emerging Roles of the RNA Binding Protein QKI in Cardiovascular Development and Function

“…As shown in Figure 2 , these emerging findings not only provide a critical understanding of QKI as an important regulator in pre-mRNA processing but also reveal its potential contribution to the pathogenesis of cardiovascular diseases and potential use as a therapeutic target in treating these diseases. Recently, QKI has been identified as one of the top 25 genes associated with aging ( Arabfard et al, 2019 ). Interestingly, an earlier genome-wide association study of a cohort with 263 cognitively intact Amish individuals at age 80 or older linked aging to 6q25-27, a chromosomal region containing the QKI gene ( Edwards et al, 2013 ).…”