  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
HighER-order 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 state-of-the-art constrained tensor/matrix factorization
models, as well as group guided and Bayesian hierarchical models. PANTHER
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 PANTHER. We
performed feature analysis in predicting disease types, which suggested
insights and benefits of the identified pathway groups.
