DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms | Zendy

Maxat Kulmanov | Zendy; Robert Hoehndorf | Zendy

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DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms

Author(s) -

Maxat Kulmanov,

Robert Hoehndorf

Publication year - 2022

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/btac256

Subject(s) - computer science , axiom , ontology , machine learning , artificial intelligence , function (biology) , set (abstract data type) , exploit , protein function prediction , gene ontology , data mining , theoretical computer science , mathematics , protein function , gene , philosophy , biochemistry , chemistry , geometry , computer security , epistemology , gene expression , evolutionary biology , biology , programming language

Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50 000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require a significant amount of training data and cannot make predictions for GO classes that have only few or no experimental annotations.

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