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Sébastien Rey

dblp:67/6397 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein function prediction
protein subcellular localization prediction
0.222010
PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes · Bioinform. 2010
PSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis · Bioinform. 2005
Bioinformatics and computational biology
protein function prediction
0.112010
PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes · Bioinform. 2010
Bioinformatics and computational biology › proteomics
proteome comparison
0.012005
PSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis · Bioinform. 2005

Methods — techniques the papers use, named apart from their topics

proteomics analysis · 0.15-fold cross-validation · 0.1support vector machine · 0.1frequent subsequence · 0.1
YearPublicationVenuePosition
2010 PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes
abstract
MOTIVATION: PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. However, the recall needs to be improved and no accurate SCL predictors yet make predictions for archaea, nor differentiate important localization subcategories, such as proteins targeted to a host cell or bacterial hyperstructures/organelles. Such improvements should preferably be encompassed in a freely available web-based predictor that can also be used as a standalone program. RESULTS: We developed PSORTb version 3.0 with improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. It features an improved standalone program, with a new batch results delivery system complementing its web interface. We evaluated the most accurate SCL predictors using 5-fold cross validation plus we performed an independent proteomics analysis, showing that PSORTb 3.0 is the most accurate but can benefit from being complemented by Proteome Analyst predictions. AVAILABILITY: http://www.psort.org/psortb (download open source software or use the web interface). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Nancy Y. Yu, James R. Wagner, Matthew R. Laird, Gabor Melli, Sébastien Rey, Raymond Lo, Phuong Dao, Süleyman Cenk Sahinalp, Martin Ester, Leonard J. Foster, Fiona S. L. Brinkman
Bioinform.5
2005 PSORTb v.2.0: Expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis
abstract
MOTIVATION: PSORTb v.1.1 is the most precise bacterial localization prediction tool available. However, the program's predictive coverage and recall are low and the method is only applicable to Gram-negative bacteria. The goals of the present work are as follows: increase PSORTb's coverage while maintaining the existing precision level, expand it to include Gram-positive bacteria and then carry out a comparative analysis of localization. RESULTS: An expanded database of proteins of known localization and new modules using frequent subsequence-based support vector machines was introduced into PSORTb v.2.0. The program attains a precision of 96% for Gram-positive and Gram-negative bacteria and predictive coverage comparable to other tools for whole proteome analysis. We show that the proportion of proteins at each localization is remarkably consistent across species, even in species with varying proteome size. AVAILABILITY: Web-based version: http://www.psort.org/psortb. Standalone version: Available through the website under GNU General Public License. CONTACT: [email protected], [email protected] SUPPLEMENTARY INFORMATION: http://www.psort.org/psortb/supplementaryinfo.html.
Jennifer L. Gardy, Matthew R. Laird, Fei Chen 0002, Sébastien Rey, C. J. Walsh, Martin Ester, Fiona S. L. Brinkman
Bioinform.4