Probabilistic methods towards understanding complex human phenotypes using genomic and healthcare data

点击翻译按钮获取中文摘要

The advent of massive biological datasets challenge existing analytic frameworks. Large genomic profiling data confer the molecular basis to link mutations to gene expression changes in specific tissues. The broad adoption of EHR systems creates rich phenotypic data including diagnostic code, lab tests, and questionnaires. These data provide promising venues for developing novel machine learning methods to elucidate the biological mechanisms that give rise to the phenotypic diversities and interdependence. However, due to the lack of scalable inference methods, existing research is often limited to analyzing only a small snapshot of the entire datasets and unable to account for the sparse, multimodal, longitudinal, irregularly sampled, and non-missing-at-random nature of the data. Our long-term vision is to develop novel machine learning methods to decipher, in a human-understandable manner, the etiology of diverse phenotypes based on genetic variants, cell-type specificities, genomic regulatory elements, gene and pathway functions, and their interactions with environments. In pursuing this vision, we propose four short-term objectives. We will develop: 1. Bayesian model to account for the multi-modality of the heterogeneous data distributions and predict composite biomarkers by associating genes, tissues, lab results, diagnosis codes via latent phenotypic topics, 2. generative model to impute correlated non-randomly missing lab results and answers to self-reported questionnaires in patients' EHR and gene expression in inaccessible tissue samples of new patients, 3. unsupervised model to infer latent trajectory of diverse patients' health states based on their longitudinal and irregularly sampled outpatient and inpatient medical records, 4. hierarchical Bayesian network that leverages the functional impacts of sequence mutations inferred from genomic data and jointly infer the directed paths from driver genetic variants, causal genes and pathways, and to phenotypes. The key innovation of our proposed methods is that, in contrast to the existing ad hoc methods, we learn all components of our proposed models simultaneously (despite their complexity) and therefore harmonize diverse datasets with complementary information. We achieve this by scalable variational inference algorithms that leverage probability theory and deep learning techniques. The proposed research will advance Bayesian learning for mining massive heterogenous data with impactful applications in medicine including composite biomarker discovery, imputation-based clinical recommendations, forecasting health trajectories, personalized risk predictions, deep interpretable models for inferring causal mutations and disease risks. Together, we present a step towards bridging the gap between the genome and the phenome by efficient Bayesian integrations of massive data, thereby improving our understanding of the cascading events from genetic mutations to a broad phenotypic spectrum.