Predicting the secondary structure of proteins using Machine Learning algorithms

• Type: Article in International Scientific Journal
• Date: 2012

Authors

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

Journal

Title: International Journal of Data Mining and Bioinformatics

Vol. 6 No. 6

Pages: 571-584

ISSN: 1748-5673

Publisher: Inderscience Publishers

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 4 Citations

COMPENDEX

Scientific classification

FOS: Natural sciences > Mathematics

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14