PANTHER: Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning
This work provides a more accurate and interpretable machine learning model for clinicians in genetic medicine by better integrating higher-order genetic features.
This paper addresses the challenge of jointly modeling genetic pathways and variants to create interpretable models for genetic medicine. The authors introduce PANTHER, a method that selects informative genetic pathways and groups them using constrained tensor factorization, then trains a softmax classifier for disease types. PANTHER significantly outperforms multiple state-of-the-art comparison models (p<0.05) on large-scale Next Generation Sequencing and whole-genome genotyping datasets.
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.