Systems Imaging & Bioinformatics Lab
Genomics and Image Informatics
Our group is interested in developing multi-modal decision algorithms that link and integrate various measurements (imaging, genomics etc) to characterize disease. Our algorithms for phenotypic measurements encompass data from 2D/3D microscopy, radiology and histopathology. We are also interested in methodological aspects of genomic analysis and image assessment. In the context of these investigations, we are very interested in collaborations with clinicians, biologists, engineers and data scientists.
Seeking Postdoctoral Fellow
The Systems Imaging and Bioinformatics Laboratory in the Department of Computational Medicine and Bioinformatics, University of Michigan has a postdoctoral fellow position available to develop analysis models for multiparametric imaging (radiology, pathology) and genomic datasets. The fellow will be responsible for development of image analysis workflows as well as multi-modal predictive models to relate image-derived phenotypes and genetics with clinical outcomes. More broadly, our group extensively uses methods from signal processing, machine learning and multi-modal data integration to study problems in health informatics & bioinformatics. More info here.
Morgan Oneka receives PICTP grant
The training program in Advanced Proteome Informatics of Cancer, funded by the National Cancer Institute, supports students performing graduate cancer-related research and provides training for proteome informatics research. The National Cancer Institute has made a substantial investment in new technology platforms for cancer proteomics, especially through the Mouse Models of Human Cancers and the Clinical Proteomic Technologies for Cancer and this investment is expected to result in greatly increased application of proteomics to cancer research. More info here.
Shariq Mohammed Presented with Precision Health Award
Shariq Mohammed, a research fellow in Computational Medicine and Bioinformatics, will use models combining both imaging and genotypic data to study time-to-recurrence for gliomas. “We aim to integrate genetic susceptibility with tumor-imaging characteristics to determine time-to-recurrence in glioma patients,” says Shariq. He explains, “Gliomas are tumors that start in the glial cells of the brain or the spine and compose about 30% of all brain and central nervous system tumors, and 80% of all malignant brain tumors. We will build statistical models to predict post-treatment time-to-recurrence, an invaluable task which will not only guide physicians in making informed personalized treatment strategies but also shed light on the biological mechanisms underlying disease progression and outcomes. We aim to develop advanced analytic tools by leveraging Precision Health datasets to enable precision discovery, and potentially precision treatment. This project will involve collaboration with experts in Neuroradiology, Bioinformatics, and Biostatistics.”