Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks

Analytical Chemistry

Volumn 77, Issue 1, 2005, Pages 78-84

  Sugimoto, Masahiro ^a,b   Kikuchi, Shinichi ^a   Arita, Masanori ^a,c,d   Soga, Tomoyoshi ^a,e   Nishioka, Takaaki ^a,f   Tomita, Maseru ^a,e  

^a KEIO UNIVERSITY   (Japan)

^b MITSUBISHI SPACE SOFTWARE CO LTD   (Japan)

^c UNIVERSITY OF TOKYO   (Japan)

^d NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^e Keio University and Human Metabolome Technologies Inc   (Japan)

^f KYOTO UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CAPILLARY ELECTROPHORESIS-MASS SPECTROMETRY (CE-MS); LARGE-SCALE PREDICTION; METABOLOME ANALYSIS; MIGRATION TIME;

CAPILLARY FLOW; COMPUTATIONAL METHODS; CORRELATION METHODS; DATABASE SYSTEMS; ELECTROPHORESIS; MASS SPECTROMETRY; NEURAL NETWORKS;

METABOLITES;

CATION;

ANALYTIC METHOD; ARTICLE; ARTIFICIAL NEURAL NETWORK; CAPILLARY ELECTROPHORESIS; CATION METABOLITE IDENTITY; CATION TRANSPORT; CHEMICAL STRUCTURE; COMPUTER ANALYSIS; COMPUTER PREDICTION; DATA BASE; ELECTROPHORETIC MOBILITY; HUMAN; INTERMETHOD COMPARISON; LIGAND BINDING; MASS SPECTROMETRY; METABOLITE;

CATIONS; ELECTROPHORESIS, CAPILLARY; MASS SPECTROMETRY; NEURAL NETWORKS (COMPUTER);

EID: 11844288302     PISSN: 00032700     EISSN: None     Source Type: Journal    
DOI: 10.1021/ac048950g     Document Type: Article

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