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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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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: 2007-04
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: eDoc: 315499
ISI: 000244879500002
 Degree: -

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Title: Current Protein & Peptide Science
  Alternative Title : Curr. Protein Pept. Sci.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 8 (2) Sequence Number: - Start / End Page: 121 - 133 Identifier: ISSN: 1389-2037