Literature DB >> 17331888

Computational classification of classically secreted proteins.

Eric W Klee1, Carlos P Sosa.   

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.

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Year:  2007        PMID: 17331888     DOI: 10.1016/j.drudis.2007.01.008

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  5 in total

1.  Computational prediction of human proteins that can be secreted into the bloodstream.

Authors:  Juan Cui; Qi Liu; David Puett; Ying Xu
Journal:  Bioinformatics       Date:  2008-08-12       Impact factor: 6.937

2.  Mass spectrometric and computational analysis of cytokine-induced alterations in the astrocyte secretome.

Authors:  Sarah Dunn Keene; Todd M Greco; Ioannis Parastatidis; Seon-Hwa Lee; Ethan G Hughes; Rita J Balice-Gordon; David W Speicher; Harry Ischiropoulos
Journal:  Proteomics       Date:  2009-02       Impact factor: 3.984

3.  The zebrafish secretome.

Authors:  Eric W Klee
Journal:  Zebrafish       Date:  2008       Impact factor: 1.985

4.  NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins.

Authors:  Daniel Restrepo-Montoya; Camilo Pino; Luis F Nino; Manuel E Patarroyo; Manuel A Patarroyo
Journal:  BMC Bioinformatics       Date:  2011-01-14       Impact factor: 3.169

5.  Validating subcellular localization prediction tools with mycobacterial proteins.

Authors:  Daniel Restrepo-Montoya; Carolina Vizcaíno; Luis F Niño; Marisol Ocampo; Manuel E Patarroyo; Manuel A Patarroyo
Journal:  BMC Bioinformatics       Date:  2009-05-07       Impact factor: 3.169

  5 in total

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