  Motivation: HIV is difficult to treat because its virus mutates at a high
rate and mutated viruses easily develop resistance to existing drugs. If the
relationships between mutations and drug resistances can be determined from
historical data, patients can be provided personalized treatment according to
their own mutation information. The HIV Drug Resistance Database was built to
investigate the relationships. Our goal is to build a model using data in this
database, which simultaneously predicts the resistance of multiple drugs using
mutation information from sequences of viruses for any new patient.
  Results: We propose two variations of a stacking algorithm which borrow
information among multiple prediction tasks to improve multivariate prediction
performance. The most attractive feature of our proposed methods is the
flexibility with which complex multivariate prediction models can be
constructed using any univariate prediction models. Using cross-validation
studies, we show that our proposed methods outperform other popular
multivariate prediction methods.
  Availability: An R package will be made available.
