Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC

Computer Methods and Programs in Biomedicine

Volumn 122, Issue 2, 2015, Pages 165-174

  Ahmad, Saeed ^a   Kabir, Muhammad ^a   Hayat, Maqsood ^a  

^a ABDUL WALI KHAN UNIVERSITY   (Pakistan)

Author keywords

Heat Shock Proteins; J protein; KNN; PNN; SVM

Indexed keywords

AMINO ACIDS; BIOLOGY; BIOSYNTHESIS; COMPUTATION THEORY; COMPUTATIONAL METHODS; PEPTIDES; SUPPORT VECTOR MACHINES; VECTOR SPACES;

AMINO ACID COMPOSITIONS; CONVENTIONAL APPROACH; DIPEPTIDE COMPOSITION; HEAT SHOCK PROTEIN; KNN; PNN; PSEUDO AMINO ACID COMPOSITIONS; SVM;

PROTEINS;

CHAPERONIN 60; DIPEPTIDE; HEAT SHOCK PROTEIN; HEAT SHOCK PROTEIN 100; HEAT SHOCK PROTEIN 20; HEAT SHOCK PROTEIN 40; HEAT SHOCK PROTEIN 70; HEAT SHOCK PROTEIN 90; J PROTEIN; UNCLASSIFIED DRUG;

AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; CLASSIFICATION ALGORITHM; DIPEPTIDE COMPOSITION; HYDROPHILICITY; HYDROPHOBICITY; K NEAREST NEIGHBOR; LEARNING ALGORITHM; PROBABILISTIC NEURAL NETWORK; PROTEIN ANALYSIS; PSEUDO AMINO ACID COMPOSITION; SPLIT AMINO ACID COMPOSITION; SUPPORT VECTOR MACHINE; ALGORITHM; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; MACHINE LEARNING; MOLECULAR GENETICS; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS;

ALGORITHMS; AMINO ACID SEQUENCE; DIPEPTIDES; HEAT-SHOCK PROTEINS; HSP70 HEAT-SHOCK PROTEINS; MACHINE LEARNING; MOLECULAR SEQUENCE DATA; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN;

EID: 84944276251     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2015.07.005     Document Type: Article

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