Machine learning in computational biology to accelerate high-throughput protein expression | Zendy

Anand V. Sastry | Zendy; Jonathan M. Monk | Zendy; Hanna Tegel | Zendy; Mathias Uhlén | Zendy; Bernhard Ø. Palsson | Zendy; Johan Rockberg | Zendy; Elizabeth Brunk | Zendy

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Machine learning in computational biology to accelerate high-throughput protein expression

Author(s) -

Anand V. Sastry,

Jonathan M. Monk,

Hanna Tegel,

Mathias Uhlén,

Bernhard Ø. Palsson,

Johan Rockberg,

Elizabeth Brunk

Publication year - 2017

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/btx207

Subject(s) - throughput , computer science , protein expression , computational biology , expression (computer science) , machine learning , artificial intelligence , biology , programming language , genetics , operating system , gene , wireless

The Human Protein Atlas (HPA) enables the simultaneous characterization of thousands of proteins across various tissues to pinpoint their spatial location in the human body. This has been achieved through transcriptomics and high-throughput immunohistochemistry-based approaches, where over 40 000 unique human protein fragments have been expressed in E. coli. These datasets enable quantitative tracking of entire cellular proteomes and present new avenues for understanding molecular-level properties influencing expression and solubility.

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