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|Title:||Deep Autoencoders for Additional Insight into Protein Dynamics|
|Keywords:||Protein molecular dynamics;Autoencoders;Unsupervised learning|
|Citation:||Lecture Notes in Computer Science, 2018, 11140 pp. 79 - 89|
|Abstract:||The study of protein dynamics through analysis of conformational transitions represents a signiﬁcant stage in understanding protein function. Using molecular simulations, large samples of protein transitions can be recorded. However,extractingfunctionalmotionsfromthesesamplesisstillnotautomated andextremelytime-consuming.Inthispaperweinvestigatetheusefulnessofunsupervised machine learning methods for uncovering relevant information about protein functional dynamics. Autoencoders are being explored in order to highlight their ability to learn relevant biological patterns, such as structural characteristics. This study is aimed to provide a better comprehension of how protein conformational transitions are evolving in time, within the larger framework of automatically detecting functional motions.|
|Appears in Collections:||Dept of Computer Science Embargoed Research Papers|
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