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Motivation: HIV is difficult to treat because its virus mutates at a highrate and mutated viruses easily develop resistance to existing drugs. If therelationships between mutations and drug resistances can be determined fromhistorical data, patients can be provided personalized treatment according totheir own mutation information. The HIV Drug Resistance Database was built toinvestigate the relationships. Our goal is to build a model using data in thisdatabase, which simultaneously predicts the resistance of multiple drugs usingmutation information from sequences of viruses for any new patient.  Results: We propose two variations of a stacking algorithm which borrowinformation among multiple prediction tasks to improve multivariate predictionperformance. The most attractive feature of our proposed methods is theflexibility with which complex multivariate prediction models can beconstructed using any univariate prediction models. Using cross-validationstudies, we show that our proposed methods outperform other popularmultivariate prediction methods.  Availability: An R package will be made available.

Simultaneous prediction of multiple outcomes using revised stacking algorithms