Achieving High Accuracy Prediction of Minimotifs

PLoS ONE

Volumn 7, Issue 9, 2012, Pages

  Mi, Tian ^a   Rajasekaran, Sanguthevar ^a   Merlin, Jerlin Camilus ^a   Gryk, Michael ^b   Schiller, Martin R ^c  

^a University of Connecticut   (United States)

^b UNIVERSITY OF CONNECTICUT HEALTH CENTER   (United States)

^c UNIVERSITY OF NEVADA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MINIMOTIF; PEPTIDE; UNCLASSIFIED DRUG;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; FALSE POSITIVE RESULT; LINEAR REGRESSION ANALYSIS; MISSENSE MUTATION; PREDICTION; PROTEIN FUNCTION; STATISTICAL MODEL; SUPPORT VECTOR MACHINE;

ALGORITHMS; AMINO ACID MOTIFS; COMPUTATIONAL BIOLOGY; INTERNET; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; PROTEIN BINDING; PROTEINS; ROC CURVE;

EID: 84866929245     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0045589     Document Type: Article

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