Deep learning for biological age estimation
Autor: | Gianfranco Doretto, Donald A. Adjeroh, Syed Ashiqur Rahman, Peter R. Giacobbi, Lee A. Pyles, Charles J. Mullett |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
0301 basic medicine
Feature engineering Aging Palliative care Computer science Process (engineering) Feature extraction Review Article Epigenesis Genetic 03 medical and health sciences 0302 clinical medicine Deep Learning Electronic Health Records Humans Molecular Biology Exercise Modalities Anthropometry business.industry Deep learning Computational Biology Data science 030104 developmental biology Paradigm shift Domain knowledge Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery Biomarkers Information Systems |
Zdroj: | Brief Bioinform |
Popis: | 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 that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health. |
Databáze: | OpenAIRE |
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