A machine learning approach for prediction of lipid-interacting residues in amino acid sequences

Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Volumn , Issue , 2007, Pages 315-319

  Irausquin, Stephanie Jiménez ^a,b   Wang, Liangjiang ^a,c  

^a CLEMSON UNIVERSITY   (United States)

^b UNIVERSITY OF SOUTH CAROLINA   (United States)

^c GREENWOOD GENETIC CENTER   (United States)

Author keywords

Lipid interacting residues; Machine learning; Sequence based prediction; Support vector machines

Indexed keywords

AMINATION; AMINES; AMINO ACIDS; ANIMAL CELL CULTURE; BIOCHEMISTRY; BIOINFORMATICS; EDUCATION; FORECASTING; IMAGE RETRIEVAL; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; LIPIDS; ORGANIC ACIDS; PATHOLOGY; ROBOT LEARNING; SUPPORT VECTOR MACHINES; VECTORS;

AMINO ACID SEQUENCES; CELLULAR LEVELS; DATA SETS; DRUG DISCOVERIES; HUMAN DISEASES; LIPID INTERACTIONS; LIPID METABOLISMS; MACHINE LEARNING; NEUROLOGICAL DISORDERS; PROTEIN DATA BANKS; PROTEIN SEQUENCES; SEQUENCE-BASED PREDICTION; SVM CLASSIFIERS;

DRUG INTERACTIONS;

EID: 47649102100     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBE.2007.4375582     Document Type: Conference Paper

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