An SVM model based on physicochemical properties to predict antimicrobial activity from protein sequences with cysteine knot motifs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6268 LNBI, Issue , 2010, Pages 59-62

  Porto, William F ^a   Fernandes, Fabiano C ^a   Franco, Octávio L ^a  

^a UNIVERSIDADE CATÓLICA DE BRASÍLIA   (Brazil)

Author keywords

Antimicrobial Peptides; Cysteine Knot Motif; Machine Learning; Physicochemical Properties; Support Vector Machine

Indexed keywords

ANTI-MICROBIAL ACTIVITY; ANTIMICROBIAL FUNCTIONS; ANTIMICROBIAL PEPTIDE; CHARGED RESIDUES; CROSS VALIDATION; CYSTEINE KNOT MOTIF; DATA SETS; MACHINE-LEARNING; NET CHARGES; PHYSICOCHEMICAL PROPERTY; POLYNOMIAL KERNELS; PROTEIN SEQUENCES; PROTEIN STRUCTURES; RANDOM SEQUENCE; SVM MODEL; TRANS-MEMBRANE PROTEINS;

AMINO ACIDS; HYDROPHOBICITY; MICROORGANISMS; PEPTIDES; STATISTICAL TESTS; SUPPORT VECTOR MACHINES; VECTORS;

BIOINFORMATICS;

EID: 78049460460     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15060-9_6     Document Type: Conference Paper

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