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Volumn 644, Issue 1-2, 2009, Pages 10-16

Predicting liquid chromatographic retention times of peptides from the Drosophila melanogaster proteome by machine learning approaches

Author keywords

Gaussian process; Least squares support vector machine; Liquid chromatography; Peptide; Quantitative structure retention relationship; Random forest

Indexed keywords

CHROMATOGRAPHIC RETENTIONS; CROSS VALIDATIONS; DESCRIPTORS; DROSOPHILA MELANOGASTER; GAUSSIAN PROCESS; LEAST-SQUARES SUPPORT VECTOR MACHINE; LINEAR METHODS; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; MODEL DEVELOPMENT; MONTE CARLO; NON-LINEAR APPROACHES; PARAMETERIZED; PARTIAL LEAST-SQUARES REGRESSIONS; PHYSICO-CHEMICAL PROPERTIES; PLS MODELS; PREDICTIVE POWER; PROTEOME; QUANTITATIVE STRUCTURE-RETENTION RELATIONSHIP; RANDOM FOREST; REGRESSION MODELS; REPRODUCIBILITY; RETENTION BEHAVIORS; RETENTION TIME; REVERSE-PHASE LIQUID CHROMATOGRAPHIES; STRUCTURAL INFORMATIONS; TEST SETS; TRAINING SETS;

EID: 65549154236     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2009.04.010     Document Type: Article
Times cited : (37)

References (34)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.