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Search Results for Research Area: "Disease modeling and analysis"


Faculty Search Results: Results 1 - 10 (List more/fewer results: 10 20 30 40 )
Faculty Member Brief Research Description
Michael Boehnke Statistical genetics, complex diseases, and human gene mapping, with application to type 2 diabetes and bipolar disorder  
Charles Burant Using multi 'omics approach, including high throughput metabolomics, we seek to understand the interaction between genetics and nutrients in the development of disease.  
Margit Burmeister Identifying genes involved in behavioral, neurological & psychiatric diseases, novel genetic techniques, gene expression, bioinformatics.  
Dan Burns DNA structure and dynamics; 4D nucleome and dynamics of chromatin related to .  
David Ferguson The Ferguson laboratory studies how mammalian cells maintain a stable genome. The proteins that accomplish this serve to prevent cancer and ensure proper functioning of the immune system. Currently, our main focus is a multi-protein complex called MRN, which is mutated in human cancer predisposition and immunodeficiency syndromes. For more information see the lab website at: http://www.pathology.med.umich.edu/fergusonlab/index.html  
Al Hero III We are developing predictive health and diagnostic algorithms for real time prediction and classification of health and disease using high throughput molecular, physiological, and behavioral data. A major axis of our research is to determine temporal immune system pathways and establish a baseline of health from multi-platform data with small sample size. Another major effort is to integrate disparate assay data, validated mathematical models, and behavioral data into inference algorithms. Molecular assays include gene microarrays, protein and amino acids, metabolites, and immunoassays. Physiological data include heart rate variability, blood pressure, and skin conductance. Behavioral data include physical activity level, sleep patterns, and social interaction. Mathematical models capture epidemiolical traits of viruses, gene ontology, and network dynamics.The analysis is validated on clinical ER data and and challenge studies.   
David M Lubman Research in the Lubman lab focuses on proteomic pathways for biomarker and drug treatment using new technologies including mass spectrometry, liquid chromatography and microarrays.  
Jordan Shavit We study the complex genetics of human blood clotting disorders. We are using CRISPR genome editing and next generation sequencing in zebrafish to develop large scale mutagenesis screens to identify genetic and chemical modifiers of these disorders.  
Kerby Shedden Statistics, including genomics, chemical biology, clinical outcome prediction, statistical genetics, and statistical image analysis.  
George Zhang Experimental: yeast evolutionary genomics Computational: evolutionary genomics and systems biology   
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