Computational classification of classically secreted proteins

Eric W. Klee; Carlos P. Sosa

doi:10.1016/j.drudis.2007.01.008

Computational classification of classically secreted proteins

Eric W. Klee, Carlos P. Sosa

Quantitative Health Sciences

Research output: Contribution to journal › Review article › peer-review

31 Scopus citations

Abstract

The ability to identify classically secreted proteins is an important component of targeted therapeutic studies and the discovery of circulating biomarkers. Here, we review some of the most recent programs available for the in silico prediction of secretory proteins, the performance of which is benchmarked with an independent set of annotated human proteins. The description of these programs and the results of this benchmarking provide insights into the most recently developed prediction programs, which will enable investigators to make more informed decisions about which program best addresses their research needs.

Original language	English (US)
Pages (from-to)	234-240
Number of pages	7
Journal	Drug Discovery Today
Volume	12
Issue number	5-6
DOIs	https://doi.org/10.1016/j.drudis.2007.01.008
State	Published - Mar 2007

ASJC Scopus subject areas

Pharmacology
Drug Discovery

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10.1016/j.drudis.2007.01.008

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AB - The ability to identify classically secreted proteins is an important component of targeted therapeutic studies and the discovery of circulating biomarkers. Here, we review some of the most recent programs available for the in silico prediction of secretory proteins, the performance of which is benchmarked with an independent set of annotated human proteins. The description of these programs and the results of this benchmarking provide insights into the most recently developed prediction programs, which will enable investigators to make more informed decisions about which program best addresses their research needs.

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Computational classification of classically secreted proteins

Abstract

ASJC Scopus subject areas

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