PANTHER: Pathway Augmented Nonnegative Tensor Factorization for HighER-order Feature Learning

Genetic pathways usually encode molecular mechanisms that can inform targeted interventions. It is often challenging for existing machine learning approaches to jointly model genetic pathways (higher-order features) and variants (atomic features), and present to clinicians interpretable models. In order to build more accurate and better interpretable machine learning models for genetic medicine, we introduce Pathway Augmented Nonnegative Tensor factorization for HighERorder feature learning (PANTHER). PANTHER selects informative genetic pathways that directly encode molecular mechanisms. We apply genetically motivated constrained tensor factorization to group pathways in a way that reflects molecular mechanism interactions. We then train a softmax classifier for disease types using the identified pathway groups. We evaluated PANTHER against multiple stateof-the-art constrained tensor/matrix factorization models, as well as group guided and Bayesian hierarchical models. PAN-THER outperforms all state-of-the-art comparison models significantly (p < 0.05). Our experiments on large scale Next Generation Sequencing (NGS) and whole-genome genotyping datasets also demonstrated wide applicability of PAN-THER. We performed feature analysis in predicting disease types, which suggested insights and benefits of the identified pathway groups.