Arvind Rao, Ph.D.

Michigan Neuroscience Institute Affiliate
Associate Professor of Biostatistics
Associate Professor of Computational Medicine and Bioinformatics
Associate Professor of Radiation Oncology

100 Washtenaw Ave.
Rm 2305
Ann Arbor, MI 48109


Areas of Interest

The Rao Research Group’s interests include Transcriptional Genomics, Image Informatics, Heterogeneous Data Integration, and Informatics for Combinatorial Drug Screens.

  • Transcriptional Genomics: a bioinformatics framework that identifies tissue‐specific enhancers by integrating multimodal genomic data that we have developed previously. We are interested in integrating other sources of information (like epigenomic and ChIP datasets) to improve the efficacy of enhancer prediction and have participated in the TCGA Glioma groups’ work on identifying transcriptional regulators underlying gliomagenesis.
  • Image Informatics: To quantify the phenotypic aspects of disease and their relationships with the outcome and their genetic context, we have developed methods for analyzing histopathology and radiology images, focusing on tumor heterogeneity. This includes developing image analysis tools to delineate tumor image features from radiology data and develop predictive models to relate them and underlying genomic measurements to outcomes in low-grade gliomas. We have also investigated methodologies to link tumor imaging, genetics, and immune status in gliomas, studying the relationship between image-derived features, genetics, and cognitive status in glioblastoma patients and developed methods for analyzing multiparametric MR datasets in Radiation Oncology.
  • 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 focus 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 the patient to assess their evolving response to treatment in the context of learning healthcare platforms. This could also enable the development of hybrid diagnostics.
  • Informatics for Combinatorial Drug Screens: the availability of multimodal data sources (cell line genomics, drug assays) coupled with high throughput/high content imaging platforms creates the need for creating informatics frameworks to identify rational drug combinations capable of modulating disease-associated phenotype. The lab has worked with the Gulf Coast Consortium to create analysis platforms that jointly mine imaging and genomics data for combinatorial drug discovery.

Published Articles or Reviews

  • Kuthuru S, Dedrick W, Bai H, Su C, Vu T, Monga V, Rao A. A Visually Interpretable, Dictionary-Based Approach to Imaging-Genomic Modeling, With Low-Grade Glioma as a Case Study. Cancer informatics. Forthcoming;
  • Kim D, Wang N, Ravikumar V, Raghuram DR, Li J, Patel A, Wendt RE 3rd, Rao G, Rao A. Prediction of 1p/19q Codeletion in Diffuse Glioma Patients Using Pre-operative Multiparametric Magnetic Resonance Imaging. Front Comput Neurosci. 2019;13:52. doi: 10.3389/fncom.2019.00052. eCollection 2019. PubMed PMID: 31417387; PubMed Central PMCID: PMC6682685.
  • Kuthuru S, Szafran AT, Stossi F, Mancini MA, Rao A. Leveraging Image-Derived Phenotypic Measurements for Drug-Target Interaction Predictions. Cancer Inform. 2019;18:1176935119856595. doi: 10.1177/1176935119856595. eCollection 2019. PubMed PMID: 31217689; PubMed Central PMCID: PMC6563400.
  • Aung PP, Parra ER, Barua S, Sui D, Ning J, Mino B, Ledesma DA, Curry JL, Nagarajan P, Torres-Cabala CA, Efstathiou E, Hoang AG, Wong MK, Wargo JA, Lazar AJ, Rao A., Prieto VG, Wistuba I, Tetzlaff MT. B7-H3 Expression in Merkel Cell Carcinoma-Associated Endothelial Cells Correlates with Locally Aggressive Primary Tumor Features and Increased Vascular Density. Clin Cancer Res. 2019 Jun 1;25(11):3455-3467. doi: 10.1158/1078-0432.CCR-18-2355. Epub 2019 Feb 26. PubMed PMID: 30808776.

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