Predicting experimental properties of proteins from sequence by machine 
learning techniques

Smialowski, P.; Martin-Galiano, A. J.; Cox, J.; Frishman, D.

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Predicting experimental properties of proteins from sequence by machine learning techniques

Smialowski, P., Martin-Galiano, A. J., Cox, J., & Frishman, D. (2007). Predicting experimental properties of proteins from sequence by machine learning techniques. Current Protein & Peptide Science, 8(2), 121-133.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-601D-B Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-601E-9

Genre: Journal Article

Alternative Title : Curr. Protein Pept. Sci.

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Creators:
Smialowski, P.¹, Author
Martin-Galiano, A. J., Author
Cox, J.², Author
Frishman, D., Author

Affiliations:
1External Organizations, ou_persistent22
2Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159

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Free keywords: structural genomics; machine learning; experimental success rate; target selection

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Language(s): eng - English

Dates: Date issued: 2007-04

Publication Status: Issued

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Rev. Type: Peer

Identifiers: eDoc: 315499
ISI: 000244879500002

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Title: Current Protein & Peptide Science

Alternative Title : Curr. Protein Pept. Sci.

Source Genre: Journal

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Affiliations:

Publ. Info: -

Pages: - Volume / Issue: 8 (2) Sequence Number: - Start / End Page: 121 - 133 Identifier: ISSN: 1389-2037