Deep learning for biological age estimation.

ABSTRACT: Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities th

SUBMITTER: Ashiqur Rahman S 

PROVIDER: S-EPMC8179516 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature