Music, Art, Machine Learning, and Standardization

Brook, Taylor

doi:10.1162/leon_a_02135

Cited by 3 publications

(1 citation statement)

References 10 publications

(7 reference statements)

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“…This new way of thinking puts computational biology at a stage where problems that were unsolvable by old methods can become solvable, or at least limited solutions can provide increased accuracy and/or speed by incorporating ML methods. The promise that ML can help solve challenging problems applies to all areas of science, engineering, and beyond (e.g., medicine, art, music, economics, public policy) [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Protein Function Analysis through Machine Learning

et al. 2022

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Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

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Section: Introductionmentioning

confidence: 99%