Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach.
Masoud ArabfardMina OhadiVahid Rezaei TabarAhmad DelbariMasoud ArabfardPublished in: BMC genomics (2019)
We 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.
Keyphrases
- genome wide
- endothelial cells
- machine learning
- big data
- electronic health record
- dna methylation
- copy number
- type diabetes
- induced pluripotent stem cells
- cardiovascular disease
- pluripotent stem cells
- papillary thyroid
- high throughput
- gene expression
- drinking water
- genome wide identification
- adipose tissue
- transcription factor
- skeletal muscle
- metabolic syndrome
- glycemic control
- deep learning
- insulin resistance
- childhood cancer