Machine learning applications are often plagued with confounders that can impact the generalizability of the learners. In clinical settings, demographic characteristics often play the role of confounders. Confounding is especially problematic in remote digital health studies where the participants self-select to enter the study, thereby making it difficult to balance the demographic characteristics of participants. One effective approach to combat confounding is to match samples with respect to the confounding variables in order to improve the balance of the data. This procedure, however, leads to smaller datasets and hence negatively impact the inferences drawn from the learners. Alternatively, confounding adjustment methods that make more efficient use of the data (such as inverse probability weighting) usually rely on modeling assumptions, and it is unclear how robust these methods are to violations of these assumptions. Here, instead of proposing a new method to control for confounding, we develop novel permutation based statistical tools to detect and quantify the influence of observed confounders, and estimate the unconfounded performance of the learner. Our tools can be used to evaluate the effectiveness of existing confounding adjustment methods. We evaluate the statistical properties of our methods in a simulation study, and illustrate their application using real-life data from a Parkinson's disease mobile health study collected in an uncontrolled environment.

MOTIVATION: Late onset Alzheimer's disease is currently a disease with no known effective treatment options. To better understand disease, new multi-omic data-sets have recently been generated with the goal of identifying molecular causes of disease. However, most analytic studies using these datasets focus on uni-modal analysis of the data. Here, we propose a data driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our article are: (i) a general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature sets and identifying other potential driver genes which have similar feature representations, and (ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. RESULTS: We demonstrate the utility of our machine learning algorithm on two benchmark multiview datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimer's. We show that our ranked genes show a significant enrichment for single nucleotide polymorphisms associated with Alzheimer's and are enriched in pathways that have been previously associated with the disease. AVAILABILITY AND IMPLEMENTATION: Source code and link to all feature sets is available at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking.