Selective of informative metabolites using random forests based on model population analysis

Talanta

Volumn 117, Issue , 2013, Pages 549-555

  Huang, Jian Hua ^a   Yan, Jun ^a   Wu, Qing Hua ^a   Duarte Ferro, Miguel ^a   Yi, Lun Zhao ^a   Lu, Hong Mei ^a   Xu, Qing Song ^a   Liang, Yi Zeng ^a  

^a CENTRAL SOUTH UNIVERSITY   (China)

Author keywords

Feature selection; Informative metabolite; Model population analysis (MPA); Random forests (RF)

Indexed keywords

DECISION TREES; FEATURE EXTRACTION; METABOLITES;

CLASSIFICATION ACCURACY; CLINICAL OUTCOME; DIAGNOSE DISEASE; METABOLOMICS; MODELPOPULATION ANALYSIS (MPA); POPULATION ANALYSIS; RANDOM FORESTS; SOURCE CODES;

BIOMOLECULES;

BIOLOGICAL MARKER;

ADULT; ANIMAL; ARTICLE; BLOOD; CASE CONTROL STUDY; CHILD; COMPUTER PROGRAM; DECISION TREE; FEATURE SELECTION; HUMAN; INFORMATIVE METABOLITE; METABOLOMICS; MILD COGNITIVE IMPAIRMENT; MODEL POPULATION ANALYSIS; MODEL POPULATION ANALYSIS (MPA); MPA; NON INSULIN DEPENDENT DIABETES MELLITUS; OBESITY; PATHOPHYSIOLOGY; RANDOM FOREST; RANDOM FORESTS (RF); RAT; RF; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS;

FEATURE SELECTION; INFORMATIVE METABOLITE; MODEL POPULATION ANALYSIS; MODEL POPULATION ANALYSIS (MPA); MPA; RANDOM FORESTS; RANDOM FORESTS (RF); RF;

ADULT; ANIMALS; BIOLOGICAL MARKERS; CASE-CONTROL STUDIES; CHILD; DECISION TREES; DIABETES MELLITUS, TYPE 2; HUMANS; METABOLOMICS; MILD COGNITIVE IMPAIRMENT; MODELS, STATISTICAL; OBESITY; RATS; SENSITIVITY AND SPECIFICITY; SOFTWARE;

EID: 84887079395     PISSN: 00399140     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.talanta.2013.07.070     Document Type: Article

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