ArticleBMC genomics2019
Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- ANGPTL4 in gestational diabetes: a diagnostic biomarker linked to placental senescence.Functional & integrative genomics · 2026Article
- Microvascular Health as a Key Determinant of Organismal Aging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Developmental dynamics of the single nucleus regulatory landscape of pig hippocampus.Science China. Life sciences · 2023Article
- Detecting biased validation of predictive models in the positive-unlabeled setting: disease gene prioritization case study.Bioinformatics advances · 2023Article
- A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women.Clinical, cosmetic and investigational dermatology · 2022Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- The Emerging Roles of the RNA Binding Protein QKI in Cardiovascular Development and Function.Frontiers in cell and developmental biology · 2021Review
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Authors and funding
5 authors.
Funding
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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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