Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries.

Boone, Kyle; Camarda, Kyle; Spencer, Paulette; Tamerler, Candan

Boone, Kyle; Camarda, Kyle; Spencer, Paulette; Tamerler, Candan.

Afiliação

Boone K; Bioengineering Program, Institute of Bioengineering Research, University of Kansas, Learned Hall, Room 5109, 1530 W 15th Street, Lawrence, KS, 66045, USA.
Camarda K; Chemical and Petroleum Engineering Department, University of Kansas, Learned Hall, Room 4154, 1530 West 15th Street, Lawrence, KS, 66045, USA.
Spencer P; Mechanical Engineering Department, Bioengineering Program, Institute of Bioengineering Research, University of Kansas, Learned Hall, Room 3111, 1530 West 15th Street, Lawrence, KS, 66045, USA.
Tamerler C; Mechanical Engineering Department, Bioengineering Program, Institute of Bioengineering Research, University of Kansas, Learned Hall, Room 3135A, 1530 W 15th St, Lawrence, KS, 66045, USA. ctamerler@ku.edu.

BMC Bioinformatics ; 19(1): 469, 2018 Dec 06.

Article em En | MEDLINE | ID: mdl-30522443

Assuntos

Peptídeos Catiônicos Antimicrobianos/classificação; Fenômenos Químicos; Peptídeos Catiônicos Antimicrobianos/metabolismo; Modelos Moleculares

Palavras-chave

Antibacterial peptides; Classification; Functional peptide search; Machine learning; Physicochemical properties; Rough set theory; Sequence similarity; Supervised learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Peptídeos Catiônicos Antimicrobianos / Fenômenos Químicos Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

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