Literature DB >> 12507786

Use of correspondence discriminant analysis to predict the subcellular location of bacterial proteins.

Guy Perrière1, Jean Thioulouse.   

Abstract

Correspondence discriminant analysis (CDA) is a multivariate statistical method derived from discriminant analysis which can be used on contingency tables. We have used CDA to separate Gram negative bacteria proteins according to their subcellular location. The high resolution of the discrimination obtained makes this method a good tool to predict subcellular location when this information is not known. The main advantage of this technique is its simplicity. Indeed, by computing two linear formulae on amino acid composition, it is possible to classify a protein into one of the three classes of subcellular location we have defined. The CDA itself can be computed with the ADE-4 software package that can be downloaded, as well as the data set used in this study, from the Pôle Bio-Informatique Lyonnais (PBIL) server at http://pbil.univ-lyon1.fr.

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Year:  2003        PMID: 12507786     DOI: 10.1016/s0169-2607(02)00011-1

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  7 in total

1.  Integrated databanks access and sequence/structure analysis services at the PBIL.

Authors:  Guy Perrière; Christophe Combet; Simon Penel; Christophe Blanchet; Jean Thioulouse; Christophe Geourjon; Julien Grassot; Céline Charavay; Manolo Gouy; Laurent Duret; Gilbert Deléage
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

2.  Analysis of regional cerebral blood flow data to discriminate among Alzheimer's disease, frontotemporal dementia, and elderly controls: a multi-block barycentric discriminant analysis (MUBADA) methodology.

Authors:  Hervé Abdi; Lynne J Williams; Derek Beaton; Mette T Posamentier; Thomas S Harris; Anjali Krishnan; Michael D Devous
Journal:  J Alzheimers Dis       Date:  2012       Impact factor: 4.472

3.  Comparative genomic analysis of three strains of Ehrlichia ruminantium reveals an active process of genome size plasticity.

Authors:  Roger Frutos; Alain Viari; Conchita Ferraz; Anne Morgat; Sophie Eychenié; Yane Kandassamy; Isabelle Chantal; Albert Bensaid; Eric Coissac; Nathalie Vachiery; Jacques Demaille; Dominique Martinez
Journal:  J Bacteriol       Date:  2006-04       Impact factor: 3.490

4.  Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): how to assign scans to categories without using spatial normalization.

Authors:  Hervé Abdi; Lynne J Williams; Andrew C Connolly; M Ida Gobbini; Joseph P Dunlop; James V Haxby
Journal:  Comput Math Methods Med       Date:  2012-04-05       Impact factor: 2.238

5.  Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data.

Authors:  Florent Baty; Michel P Bihl; Guy Perrière; Aedín C Culhane; Martin H Brutsche
Journal:  BMC Bioinformatics       Date:  2005-09-28       Impact factor: 3.169

6.  Comprehensive Insights Into Composition, Metabolic Potentials, and Interactions Among Archaeal, Bacterial, and Viral Assemblages in Meromictic Lake Shunet in Siberia.

Authors:  Yu-Ting Wu; Cheng-Yu Yang; Pei-Wen Chiang; Ching-Hung Tseng; Hsiu-Hui Chiu; Isaam Saeed; Bayanmunkh Baatar; Denis Rogozin; Saman Halgamuge; Andrei Degermendzhi; Sen-Lin Tang
Journal:  Front Microbiol       Date:  2018-08-20       Impact factor: 5.640

7.  Immunological Classification of Pancreatic Carcinomas to Identify Immune Index and Provide a Strategy for Patient Stratification.

Authors:  Yi Chen; Didi Chen; Qiang Wang; Yajing Xu; Xiaowei Huang; Felix Haglund; Huafang Su
Journal:  Front Immunol       Date:  2022-01-17       Impact factor: 7.561

  7 in total

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