Work

Sparse Representation for HIV-1 Protease Drug Resistance Prediction

Year: 2013

Type: conference-paper

Abstract: HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5 × 104 known sequences (Stanford HIV database: http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy.

Source: SIAM International Conference on Data Mining

DOI: 10.1137/1.9781611972832.38

Authors: Xiaxia Yu, Irene Weber, Robert Harrison

Institution: Georgia State University

Language: English

Topic: HIV/AIDS drug development and treatment

Subfield: Infectious Diseases

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of General Medical Sciences