Báo cáo y học: "HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học 'Respiratory Research cung cấp cho các bạn kiến thức về ngành y đề tài: "HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels. | Retrovirology BioMed Central Research HIV-1 coreceptor usage prediction without multiple alignments an application of string kernels Sébastien Boisvert1 Mario Marchand2 Francois Laviolette2 and Jacques Corbeil 1 Open Access Address 1Centre de recherche du centre hospitalier de l Université Laval Québec QC Canada and 2Département d informatique et de génie logiciel Université Laval Québec QC Canada Email Sébastien Boisvert - Mario Marchand - Francois Laviolette - Jacques Corbeil - Corresponding author Published 4 December 2008 Received 14 July 2008 Accepted 4 December 2008 Retrovirology 2008 5 110 doi 1742-4690-5-110 This article is available from http content 5 1 1 10 2008 Boisvert et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Human immunodeficiency virus type 1 HIV-1 infects cells by means of ligandreceptor interactions. This lentivirus uses the CD4 receptor in conjunction with a chemokine coreceptor either CXCR4 or CCR5 to enter a target cell. HIV-1 is characterized by high sequence variability. Nonetheless within this extensive variability certain features must be conserved to define functions and phenotypes. The determination of coreceptor usage of HIV-1 from its protein envelope sequence falls into a well-studied machine learning problem known as classification. The support vector machine SVM with string kernels has proven to be very efficient for dealing with a wide class of classification problems ranging from text categorization to protein homology detection. In this paper we investigate how the SVM can predict HIV-1 coreceptor usage

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