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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.

Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests