caa a22 4500 378926071 CHVBK 20180305123617.0 cr unu---uuuuu 161128e20040212xx s 000 0 eng 10.2202/1544-6115.1031 doi (NATIONALLICENCE)gruyter-10.2202/1544-6115.1031 Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests [Elektronische Daten] [Mark R Segal, Jason D Barbour, Robert M Grant] The problem of relating genotype (as represented by amino acid sequence) to phenotypes is distinguished from standard regression problems by the nature of sequence data. Here we investigate an instance of such a problem where the phenotype of interest is HIV-1 replication capacity and contiguous segments of protease and reverse transcriptase sequence constitutes genotype. A variety of data analytic methods have been proposed in this context. Shortcomings of select techniques are contrasted with the advantages afforded by tree-structured methods. However, tree-structured methods, in turn, have been criticized on grounds of only enjoying modest predictive performance. A number of ensemble approaches (bagging, boosting, random forests) have recently emerged, devised to overcome this deficiency. We evaluate random forests as applied in this setting, and detail why prediction gains obtained in other situations are not realized. Other approaches including logic regression, support vector machines and neural networks are also applied. We interpret results in terms of HIV-1 reverse transcriptase structure and function. ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston Multivariate Analysis nationallicence Protease nationallicence Random Forests nationallicence Reverse Transcriptase nationallicence Tree-Structured Methods nationallicence Segal Mark R. University of California, San Francisco aut Barbour Jason D. University of California, San Francisco aut Grant Robert M. University of California, San Francisco aut Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-02-12), 1-18 3:1<1 2004 3 sagmb https://doi.org/10.2202/1544-6115.1031 text/html Onlinezugriff via DOI 1 research article jats NATIONALLICENCE 856 40 https://doi.org/10.2202/1544-6115.1031 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Segal Mark R. University of California, San Francisco aut NATIONALLICENCE 700 1- Barbour Jason D. University of California, San Francisco aut NATIONALLICENCE 700 1- Grant Robert M. University of California, San Francisco aut NATIONALLICENCE 773 0- Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-02-12), 1-18 3:1<1 2004 3 sagmb CC0 http://creativecommons.org/publicdomain/zero/1.0 nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-gruyter