Machine learning approaches for discrimination of Extracellular Matrix proteins using hybrid feature space

Journal of Theoretical Biology

Volumn 403, Issue , 2016, Pages 30-37

  Ali, Farman ^a   Hayat, Maqsood ^a  

^a ABDUL WALI KHAN UNIVERSITY   (Pakistan)

Author keywords

Di peptide composition; Extracellular Matrix; K Nearest Neighbor; PseAAC

Indexed keywords

SCLEROPROTEIN; AMINO ACID; PEPTIDE;

AMINO ACID; CHEMICAL COMPOSITION; MACHINE LEARNING; NEAREST NEIGHBOR ANALYSIS; PROTEIN;

ACCURACY; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ANGIOGENESIS; ARTICLE; CELL ADHESION; CELL DIFFERENTIATION; CELL MIGRATION; CELL PROLIFERATION; CLASSIFICATION ALGORITHM; CLASSIFIER; CONTROLLED STUDY; DIPEPTIDE COMPOSITION; EMBRYO DEVELOPMENT; EXTRACELLULAR MATRIX; GENE EXPRESSION; HUMAN; HYBRID; K NEAREST NEIGHBOR; MACHINE LEARNING; PRIORITY JOURNAL; PROTEIN ANALYSIS; PSEUDO AMINO ACID COMPOSITION; RANDOM FOREST; RECALL; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TUMOR GROWTH; ALGORITHM; CHEMISTRY; METABOLISM; PROTEIN DATABASE;

ALGORITHMS; AMINO ACIDS; DATABASES, PROTEIN; EXTRACELLULAR MATRIX PROTEINS; MACHINE LEARNING; PEPTIDES;

EID: 84967214935     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2016.05.011     Document Type: Article

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