Washington University School of Medicine, St. Louis, 1999-2000 Post-doctoral study in Medical Imaging Xi'an Jiaotong University, School of Electrical and Information Engineering, P. R. China, 1996 Ph.D. in Communications and Electronic Systems Xi'an Jiaotong University, Xi'an, Faculty of Science, 1993 P.R. China, M.S. in Computational Mathematics Tianjin University, Tianjin, P.R. China, 1990 B.S. in Applied Mathematics
The Multiscale Bioimaging and Bioinformatics Laboratory at Tulane University has three research themes: 1. Fundamental research on multiscale signal/image representation and analysis; 2. Multiscale bioimaging analysis from organ and tissue levels to molecular and cellular levels; and 3. Bioinformatics in human genomics and cytogenetics. Currently, we are working on information extraction and integration from multiscale and multimodality genomic imaging data. One of our goals is to bring the biomedical technique into commercial use. We are using a multidisciplinary approach and working closely with computational scientists, statisticians, medical geneticists and industrial engineers at Tulane Medical Center and all over the world.
J. Sheng, V. Calhoun, H.W. Deng and Y.-P Wang, An integrated analysis of gene expression and copy number data on gene shaving using independent component analysis, IEEE Trans. Computational Biology and Bioinformatics, in press, 2011.
J. Chen, Ayten Yiğiter,Y.-P. Wang, and H.-W. Deng, A Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process, J. Bioinformatics and Systems Biology, Vol. 2010 (2010).
Jie Chen and Y.-P. Wang, A statistical model-based approach for the identification of DNA copy number changes in array CGH datasets, IEEE Trans. Computational Biology and Bioinformatics, 6(4), Oct.-Dec. issue, 2009.
Yu-Ping Wang, Multiscale genomic imaging informatics, IEEE Signal Processing Magazine, pp.169-172, 2009.
Y.-P. Wang, M. Gunampally, J. Chen, D. Bittel, M. Butler and W.-W. Cai, A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression, Journal of VLSI Signal Processing Special Issue on Machine Learning for Microarray and Sequence Analysis, 2007.
Yu-Ping Wang, Q. Wu, Ken. Castleman, Z. Xiong , Chromosome Image Enhancement Using Multiscale Differential Operators, IEEE Trans. Medical Imaging, vol. 22, no.5, May, 2003.
Yu-Ping Wang and Ashok Dandpat, A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multi-spectral Florescence in Situ Hybridization Images, Int. Journal of Biomedical Imaging, vol. 2006, pp. 1-11, 2006.
Yu-Ping Wang, Husain Ragib, and Chi-Ming Huang, A wavelet approach for the identification of axonal synaptic varicosities from microscope images, IEEE Trans. Information Technology in Biomedicine, 11(3): 296-304, May, 2007.
Yu-Ping Wang and Wei-Wen Cai, Genetic imaging: where imaging science meets cytogenetic research, Biophotonics Magazine, Nov., 2004.
Yu-Ping Wang, Q. Wu, Ken. Castleman, Z. Xiong, Chromosome Image Enhancement Using Multiscale Differential Operators , IEEE Trans. Medical Imaging, vol. 22, no.5, May, 2003.
NIH 1R21LM010042-01, Wang Yu-Ping (PI) – 07/1/2009-06/30/2011
A New Paradigm for Integrated Analysis of Multiscale Genomic Imaging Datasets.
The goal of the project is to develop a public accessible database system that can combine structural and functional image datasets for systems biology analysis.
NIH 1R15GM088802-01, Wang Yu-Ping (PI) – 09/21/2009-08/30/2012
Accurate detection of chromosomal abnormalities with multi-color image processing.
The goal of the project is to develop image processing algorithms for the detection of chromosome aberrations with clinical validations.
NSF, DBI 0849932, Wang Yu-Ping (PI) – 12/01/2009-11/30/2012
Multiscale Genomic Imaging Informatics.
A theory and database systems will be proposed for systems biology analysis of multiscale biological systems.
Ladies Leukemia League Grant: Wang Yu-Ping (PI) – 05/1/2011-04/30/2012
Bioinformatics technique for accurate subtype classification of myelodysplastic syndrome (MDS).
The project will develop bioinformatics approach for subtyping of MDS for personalized diagnosis