Unb-DPC: Identify mycobacterial membrane protein types by incorporating un-biased dipeptide composition into Chou's general PseAAC

Journal of Theoretical Biology

Volumn 415, Issue , 2017, Pages 13-19

  Khan, Muslim ^a   Hayat, Maqsood ^a   Khan, Sher Afzal ^a,b   Iqbal, Nadeem ^a  

^a ABDUL WALI KHAN UNIVERSITY   (Pakistan)

^b KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Mycobacterium; Oversampled features; Support vector machine; Un biasness

Indexed keywords

MEMBRANE PROTEIN; DIPEPTIDE;

ALGORITHM; BACTERIUM; MEMBRANE; PREDICTION; PROTEIN; SUPPORT VECTOR MACHINE;

AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; HYDROPHOBICITY; JACKKNIFE TEST; K NEAREST NEIGHBOR; MEASUREMENT ACCURACY; MEASUREMENT PRECISION; MYCOBACTERIUM; NONHUMAN; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN STRUCTURE; RANDOM FOREST; SPLIT AMINO ACID COMPOSITION; SUPPORT VECTOR MACHINE; UNBIASED DIPEPTIDE COMPOSITION; ALGORITHM; BIOLOGY; CHEMISTRY; CLASSIFICATION; MYCOBACTERIACEAE; PROCEDURES; STATISTICAL BIAS; THEORETICAL MODEL; ULTRASTRUCTURE;

BACTERIA (MICROORGANISMS); MYCOBACTERIUM;

ALGORITHMS; AMINO ACID SEQUENCE; BIAS (EPIDEMIOLOGY); COMPUTATIONAL BIOLOGY; DIPEPTIDES; MEMBRANE PROTEINS; MODELS, THEORETICAL; MYCOBACTERIACEAE;

EID: 85002575472     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2016.12.004     Document Type: Article

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