DeepQA: A Unified Transcriptome-Based Aging Clock Using Deep Neural Networks.

ABSTRACT: Understanding the complex biological process of aging is of great value, especially as it can help develop therapeutics to prolong healthy life. Predicting biological age from gene expression data has shown to be an effective means to quantify aging of a subject, and to identify molecular and cellular biomarkers of aging. A typical approach for estimating biological age, adopted by almost all existing aging clocks, is to train machine learning models only on healthy subjects, but to infer on both healthy and unhealthy subjects. However, the inherent bias in this approach results in inaccurate biological age as shown in this study. Moreover, almost all existing transcriptome-based aging clocks were built around an inefficient procedure of gene selection followed by conventional machine lear

SUBMITTER: Qi H 

PROVIDER: S-EPMC12074024 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature