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dc.contributor.authorWoods, Matthewen_US
dc.contributor.authorCarpenter, Gail A.en_US
dc.date.accessioned2011-11-14T18:17:03Z
dc.date.available2011-11-14T18:17:03Z
dc.date.issued2007-02en_US
dc.identifier.urihttps://hdl.handle.net/2144/1942
dc.description.abstractThis article presents a new method for predicting viral resistance to seven protease inhibitors from the HIV-1 genotype, and for identifying the positions in the protease gene at which the specific nature of the mutation affects resistance. The neural network Analog ARTMAP predicts protease inhibitor resistance from viral genotypes. A feature selection method detects genetic positions that contribute to resistance both alone and through interactions with other positions. This method has identified positions 35, 37, 62, and 77, where traditional feature selection methods have not detected a contribution to resistance. At several positions in the protease gene, mutations confer differing degress of resistance, depending on the specific amino acid to which the sequence has mutated. To find these positions, an Amino Acid Space is introduced to represent genes in a vector space that captures the functional similarity between amino acid pairs. Feature selection identifies several new positions, including 36, 37, and 43, with amino acid-specific contributions to resistance. Analog ARTMAP networks applied to inputs that represent specific amino acids at these positions perform better than networks that use only mutation locations.en_US
dc.description.sponsorshipAir Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624)en_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-2007-004en_US
dc.rightsCopyright 2007 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.titleNeural Network and Bioinformatic Methods for Predicting HIV-1 Protease Inhibitor Resistanceen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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