A co-training algorithm for multi-view data with applications in data fusion

In several scientific applications, data are generated from two or more diverse sources (views) with the goal of predicting an outcome of interest. Often it is the case that the outcome is not associated with any single view. However, the synergy of all measurements from each view may yield a more predictive classifier. For example, consider a drug discovery application in which individual molecules are described partially by several assay screens based on diverse profiles and partially by their chemical structural fingerprints. A common classification problem is to determine whether the molecule is associated with a particular disease. In this article, a co-training algorithm is developed to utilize data from diverse sources to predict the common class variable. Novel enhancements for variable importance, robustness to a mislabeled class variable, and a technique to handle unbalanced classes are applied to the motivating data set, highlighting that the approach attains strong performance and provides useful diagnostics for data analytic purposes. In addition, comparisons to a framework with data fusion using PLS are also assessed on real data. An R package for performing the proposed approach is provided as supplementary material.