This is a proposal to plan and develop a Program of Excellence in computational biology from genotype to phenotype. This planning grant application includes development projects focusing on mapping brain phenotype, mapping biological structure and mapping genomic function. These will be supported by well-developed Cores: Administration, Computational Resources and Education & Training. The program is an integrated effort with multidisciplinary participants from several different departments including mathematics, computer science, neurology, genetics and biochemistry. The Institute of Pure and Applied Mathematics and the Laboratory of Neuro Imaging provide physical resources and additional infrastructure necessary to create this program. The development projects and the cores each provide a symbiotic contribution from both computation and biology. These projects were established because of their mutually beneficial relationship with one another, as well as with the core support activities and the training and educational opportunities of this campus. Plans include collaboration with other investigators and the creation and nurturing of mini-projects that will emerge and grow to full-fledged development projects. Mapping Brain Phenotype will utilize novel mathematical and computational strategies for analyzing whole brain images to understand brain variability and detecting abnormal patterns. Mapping Biological Structure will utilize partial differential equation based image processing techniques in the study of tomographic data, blood flow simulation, gene expression, micro-array segmentation and geometric computations of brain mapping. Mapping Genomic Function will develop statistical methods for measuring the strength of evidence directly from genomics raw data. The Administration Core will provide the unified coordination necessary to propel a complex program such as proposed here to the next stage as a full-fledged Center of Excellence. The Computational Resources Core will develop resources and make them available to the other entities of this program. The Education and Training Core includes plans to train undergraduate and graduate students, postdoctoral fellows, visiting scientists and faculty from other departments. An outreach component is also included. The focus of this proposal remains fixed on the computational biology of brain and yet the developments that will emerge from this Center of Excellence will have a broad and far-reaching applicability to other disciplines of science.
| Mittal, Saumya H; Goel, Deepak; Mittal, Manish et al. (2015) Identification of Mortality-related Predictive Factors in Hospitalized Patients with Ischemic Stroke. Astrocyte 1:272-276 |
| Dinov, Ivo D; Petrosyan, Petros; Liu, Zhizhong et al. (2014) The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools. Brain Imaging Behav 8:311-22 |
| Dong, Bin; Chien, Aichi; Mao, Yu et al. (2008) Level set based surface capturing in 3D medical images. Med Image Comput Comput Assist Interv 11:162-9 |
| Vidal, Christine N; Nicolson, Rob; Boire, Jean-Yves et al. (2008) Three-dimensional mapping of the lateral ventricles in autism. Psychiatry Res 163:106-15 |
| Gilboa, Guy (2008) Nonlinear scale space with spatially varying stopping time. IEEE Trans Pattern Anal Mach Intell 30:2175-87 |
| Shi, Yonggang; Thompson, Paul M; de Zubicaray, Greig I et al. (2007) Direct mapping of hippocampal surfaces with intrinsic shape context. Neuroimage 37:792-807 |
| Vidal, Christine N; Nicolson, Rob; DeVito, Timothy J et al. (2006) Mapping corpus callosum deficits in autism: an index of aberrant cortical connectivity. Biol Psychiatry 60:218-25 |
| Gilboa, Guy; Sochen, Nir; Zeevi, Yehoshua Y (2006) Variational denoising of partly textured images by spatially varying constraints. IEEE Trans Image Process 15:2281-9 |
| Luders, E; Narr, K L; Zaidel, E et al. (2006) Parasagittal asymmetries of the corpus callosum. Cereb Cortex 16:346-54 |
| Yip, Andy M; Ding, Chris; Chan, Tony F (2006) Dynamic cluster formation using level set methods. IEEE Trans Pattern Anal Mach Intell 28:877-89 |
Showing the most recent 10 out of 44 publications
