Auslander, N.; Gussow, A.B.; Koonin, E.V. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int. J. Mol. Sci.2021, 22, 2903.
Auslander, N.; Gussow, A.B.; Koonin, E.V. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int. J. Mol. Sci. 2021, 22, 2903.
Auslander, N.; Gussow, A.B.; Koonin, E.V. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int. J. Mol. Sci.2021, 22, 2903.
Auslander, N.; Gussow, A.B.; Koonin, E.V. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int. J. Mol. Sci. 2021, 22, 2903.
Abstract
The exponential growth of biomedical data in recent years urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling automatic feature extraction, selection and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology and disease genomics. We outline the challenges posed for machine learning, and in particular, deep learning in biomedicine and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.
Keywords
machine learning; deep learning; bioinformatics; phylogenetics; cancer evolution
Subject
Biology and Life Sciences, Anatomy and Physiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
