Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework | Zendy

Lewis Moffat | Zendy; David T. Jones | Zendy

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Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework

Author(s) -

Lewis Moffat,

David T. Jones

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab491

Subject(s) - computer science , leaps , artificial intelligence , source code , machine learning , documentation , blueprint , software , sequence (biology) , deep learning , field (mathematics) , open source , data mining , simple (philosophy) , programming language , biology , mechanical engineering , mathematics , financial economics , pure mathematics , engineering , economics , genetics , philosophy , epistemology

Over the past 50 years, our ability to model protein sequences with evolutionary information has progressed in leaps and bounds. However, even with the latest deep learning methods, the modelling of a critically important class of proteins, single orphan sequences, remains unsolved.

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