Working Paper Review Version 1 This version is not peer-reviewed

Incorporating Machine Learning Into Bioinformatics Frameworks

Version 1 : Received: 15 February 2021 / Approved: 17 February 2021 / Online: 17 February 2021 (09:40:45 CET)

A peer-reviewed article of this Preprint also exists.

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.

Journal reference: Int. J. Mol. Sci. 2021, 22, 2903
DOI: 10.3390/ijms22062903

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

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