Learning representations from heterogeneous data for digital health

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Molecular biomarkers, including genes, miRNAs, LncRNAs, circRNAs, etc., not only help understand the mechanisms of complex human diseases, but also help their diagnosis, prognosis, treatment and drug development. The emerging high throughput technologies generate copious heterogeneous data such as sequence data, expression data, semantic data, and some experimentally verified association(network) data. Although existing machine learning methods can be applied for various prediction tasks from such heterogeneous data, there are some challenges and limitations, as well as some obstacles yet to overcome, including lack of integration of heterogeneous data, lack of balanced training data, and lack of model explanation or intelligibility. In this proposed research program my long-term goal is to develop the novel artificial intelligence models and algorithms for effectively and efficiently learning strong comprehensive representations of biomarkers, drugs and diseases from heterogeneous data while applying them to various prediction tasks, which are key to digital health for precision medicine. To achieve my long-term goal, three specific objectives are designed in this proposal as follows. Objective 1: Developing improved machine learning models for learning representations of biomarkers, drugs and diseases by integrating heterogeneous data; Objective 2: Developing deep neural network models for learning representations of biomarkers, drugs and diseases from heterogeneous data. Objective 3: Developing intelligible machine learning models that can learn the representations of biomarkers, drugs and diseases for interpretable prediction results, so that humans can understand and interpret the outcomes. Our developed models and algorithms will be applied to several prediction tasks such as gene-disease associations, miRNA-disease associations, circRNA-disease associations, LncRNA-disease associations or dug repositioning. This proposed research program will make advances in both artificial intelligence and digital health that will ultimately aid in processing, analyzing, understanding, and exploiting heterogeneous health data. The methods to be developed could also be used by researchers in other areas (natural language processing, image analytics, just to name a few) in guiding their methods and discovering new knowledge through our proposed knowledge translation strategies. Through this proposed research program, three Ph.D., and several M.Sc. and undergraduate students will be trained to gain advanced knowledge in artificial intelligence, digital health, and software development, as well as the problem solving skills in digital health and other fields in which artificial intelligence is indispensable. In addition, high demand in artificial intelligence and digital health combined with my collaborative research approach provides my students with numerous opportunities for working in this exciting multidisciplinary training environment.