AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties

Journal of Theoretical Biology

Volumn 270, Issue 1, 2011, Pages 56-62

  Kandaswamy, Krishna Kumar ^a   Chou, Kuo Chen ^b   Martinetz, Thomas ^a   Möller, Steffen ^a   Suganthan, P N ^c   Sridharan, S ^d   Pugalenthi, Ganesan ^c,e  

^a UNIVERSITY OF LÜBECK   (Germany)

^b GORDON LIFE SCIENCE INSTITUTE   (United States)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^d BHARATHIDASAN UNIVERSITY   (India)

^e GENOME INSTITUTE OF SINGAPORE   (Singapore)

Author keywords

Freeze tolerance; Ice binding proteins; Machine learning method; Physicochemical properties; Thermal hysteresis proteins

Indexed keywords

ANTIFREEZE PROTEIN;

BODY FLUID; FREEZING; HYSTERESIS; PHYSICOCHEMICAL PROPERTY; PREDICTION; PROTEIN; TEMPERATURE TOLERANCE;

ACCURACY; AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; GENOMICS; INTERMETHOD COMPARISON; PREDICTION; PRIORITY JOURNAL; PROTEOMICS; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SEQUENCE DATABASE; SEQUENCE HOMOLOGY;

ALGORITHMS; AMINO ACID SEQUENCE; AMINO ACIDS; ANTIFREEZE PROTEINS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; PHYSICOCHEMICAL PHENOMENA; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; PROTEINS; ROC CURVE;

FUNGI; INVERTEBRATA; VERTEBRATA;

EID: 78649326452     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2010.10.037     Document Type: Article

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