Predicting the secondary structure of proteins using Machine Learning algorithms

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

Autores

Rui Camacho (Autor) FEUP; Rita Ferreira (Autor) Outra; Natacha Rosa (Autor) Outra; Vânia Guimarães (Autor) Outra; Nuno A Fonseca (Autor) FCUP; Vítor Santos Costa (Autor) FCUP; Miguel de Sousa (Autor) Outra; Alexandre Magalhaes (Autor) FCUP

Revista

Título: International Journal of Data Mining and Bioinformatics

Vol. 6 Nº 6

Páginas: 571-584

ISSN: 1748-5673

Editora: Inderscience Publishers

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 4 Citações

COMPENDEX

Classificação Científica

FOS: Ciências exactas e naturais > Matemática

Outras Informações

ID Authenticus: P-002-ETH

DOI: 10.1504/ijdmb.2012.050265

Abstract (EN): The functions of proteins in living organisms are related to their 3-D structure, which is known to be ultimately determined by their linear sequence of amino acids that together form these macromolecules. It is, therefore, of great importance to be able to understand and predict how the protein 3D-structure arises from a particular linear sequence of amino acids. In this paper we report the application of Machine Learning methods to predict, with high values of accuracy, the secondary structure of proteins, namely alpha-helices and beta-sheets, which are intermediate levels of the local structure.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14