Joshua Stein

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Josua Stein is an associate professor of Ophthalmology and Visual Sciences at Michigan Medicine. He is the principal investigator of the Sight Outcomes Research Collaborative (SOURCE) consortium, a group of academic ophthalmology departments who are extracting EHR data and data from ocular diagnostic tests for all eye care recipients at their respective institutions, removing all PHI, and sending their data to the University of Michigan where him and his team are cleaning and aggregating the data and making it available to researchers at the various sites for research and Q/I projects. The team is integrating data from SOURCE into machine learning algorithms, applying systems engineering techniques to forecast disease trajectory for chronic eye diseases such as glaucoma, and other precision medicine initiatives.

Rudy Richardson

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Rudy Richardson is a Professor Emeritus of Environmental Health Sciences, in the School of Public Health and of Neurology and Toxicology in Michigan Medicine. He runs the computational molecular modeling lab and is certified by the American Board of Toxicology. He works on computational/predictive toxicology including computational studies on medicinal chemistry projects focused on discovery of therapeutic agents for Alzheimer’s disease and other neurodegenerative disorders. He remains fully active in research and selective mentoring of students and postdoctoral fellows.

Arvind Rao

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Arvind Rao is an Associate Professor in the Department of Computational Medicine and Bioinformatics, and Radiation Oncology in the School of Medicine. 

His research is in:

1. Transcriptional Genomics: A bioinformatics framework that identifies tissue‐specific enhancers by integrating multi‐modal genomic data has been developed previously [Rao2010]. There is interest to integrate other sources of information (like epigenomic and ChIP datasets) to improve the efficacy of enhancer prediction. We have also participated in the TCGA Glioma groups’ work [Brat2015, Ceccarelli2016] on identifying transcriptional regulators underlying gliomagenesis.

2. Image Informatics: In order to quantify the phenotypic aspects of disease and their relationships with outcome and their genetic context, we have developed methods for the analysis of histopathology [ Mousavi2015, Vu2016] and radiology [Yang2015] images, focusing on tumor heterogeneity. One direction of our group is to develop image analysis tools to delineate tumor image features from radiology data and to develop predictive models to relate them along with underlying genomic measurements to outcomes in low grade gliomas. Further, we have also investigated methodologies to link tumor imaging, genetics and immune status in gliomas. More recently, my group has been studying the relationship between image-derived features, genetics and cognitive status in glioblastoma patients. Further, we have also developed methods for the analysis of multiparametric MR datasets in Radiation Oncology.

3. Heterogeneous Data Integration: Integrative decision making in the clinical domain involves the need for principled formalisms that can integrate pathology, imaging and genomic data sets to drive hypothesis generation and clinical action. We have focused on developing high throughput measurement pipelines from this diverse array of data sources and methods for their integration. Simultaneously, methods for visualization are also under investigation. A more recent interest of our group is to integrate genomics, imaging and (online) behavioral data from patient to assess their evolving response to treatment, in the context of learning healthcare platforms. This could also enable the development of hybrid diagnostics.

4. Informatics for Combinatorial Drug Screens: the availability of multimodal data sources (cell line genomics, drug assays) coupled with high throughput, high content imaging platforms have created the need for informatics frameworks to identify rational drug combinations capable of modulating disease-associated phenotype. In this context, we have worked with the Gulf Coast Consortium to create analysis platforms that jointly mine imaging and genomics data for combinatorial drug discovery.


The overall goal is to link different data sources, such as imaging-derived phenotypes with genomic alteration for clinical predictive models. This has prompted work in AI/ML models for image processing &computer vision, data integration and genomic analysis.