Missing value imputation in high-dimensional phenomic data: Imputable or not, and how?

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Liao, Serena G ^a   Lin, Yan ^a   Kang, Dongwan D ^a   Chandra, Divay ^d   Bon, Jessica ^d   Kaminski, Naftali ^d   Sciurba, Frank C ^c   Tseng, George C ^a,b,c  

^a UNIVERSITY OF PITTSBURGH GRADUATE SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF PITTSBURGH   (United States)

^d YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

K nearest neighbor; Missing data; Phenomic data; Self training selection

Indexed keywords

AUTOCORRELATION; BIOINFORMATICS; DECISION TREES; MAMMALS; MOTION COMPENSATION; THROUGHPUT;

HIGH THROUGHPUT EXPERIMENTS; IMPUTATION OF MISSING VALUES; K-NEAREST NEIGHBOR METHOD; K-NEAREST NEIGHBORS; MISSING DATA; MISSING DATA IMPUTATIONS; PHENOMIC DATA; SELF TRAINING;

NEAREST NEIGHBOR SEARCH; AUTOCORRELATION;

ALGORITHM; BIOLOGY; CLUSTER ANALYSIS; COMPUTER PROGRAM; COMPUTER SIMULATION; EPIDEMIOLOGY; HUMAN; INFORMATION PROCESSING; METHODOLOGY;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATASETS AS TOPIC; EPIDEMIOLOGIC METHODS; HUMANS; RESEARCH DESIGN; SOFTWARE;

EID: 84920822273     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-014-0346-6     Document Type: Article

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