Research Topics
- Addiction
- Aging
- Alzheimer's Disease
- Anti-Tumor Therapy
- Autism
- Bioinformatics
- Brain
- Cancer
- Cancer Genetics
- Cell Cycle
- Cell Division
- Cerebral Cortex
- Cognitive Neuroscience
- Diabetes
- Gene Discovery
- Gene Expressions
- Gene Regulation
- Gene Therapy
- Genetics
- Genomics
- Human Genetics and Genetic Disorders
- Image Analysis
- Inflammation
- Liver
- Lung
- Metastasis
- Microarray
- Microglia
- Molecular Biology
Training Areas
Education
M.S., Tsinghua University
Electronic EngineeringB.E., Tongji University
Electrical EngineeringM.S., State University of New York at Buffalo
Computer SciencePh.D., State University of New York at Buffalo
Computer Science
Biography
Dr. Zhang is currently an associate professor of the Department of Genetics and Genomic Sciences and a member of Institute of Genomics and Multiscale Biology.
Dr. Zhang’s extensive experience in electrical engineering, computer science and computational biology empowers him to build up highly predictive models for very complex data from handwritten document images to large-scale cancer genomic data. Over the past few years, Dr. Zhang has developed and significantly contributed a series of influential gene network inference algorithms which have been extensively used for identification of novel pathways and gene targets, as well as development of drugs for a variety of human diseases such as cancer, atherosclerosis, Alzheimer's, obesity and diabetes. His latest research that sheds a new light on targeted therapies against breast cancer was featured by the Second AACR International Conference on Frontiers in Basic Cancer Research (San Francisco, September 14-18, 2011). His recent work on predicting genetic interactions was identified by Nature Biotechnology as one of the breakthroughs in the field of computational biology in 2010. The discovery of a gene cluster that is causally linked to obesity and diabetes was highlighted in Nature in 2008. His early research on image pattern recognition significantly contributed to several large-scale pattern recognition systems including U.S. Handwritten Address Identification System which has been adopted by US Postal Office. Dr. Zhang was a recipient of the Best Paper Award of ICDAR 2003 ─ the Seventh International Conference on Document Analysis and Recognition.
As a prolific researcher, Dr. Zhang has published a number of high profile papers in Nature, Nature Genetics and PNAS. As of December 2011, his publications have been cited 2750 times, according to Google Scholar. Furthermore, he has been a leader of more than a dozen projects to identify novel drug targets for several pharmaceutical companies.
Research
Identification of Synthetic Lethal Interactions for Cancer Therapy
Identification of synthetic lethal (SL) interactions in human disease like cancer has a great potential to improve targeted therapies by targeting only genes having SL interactions with those mutated genes. Improved high-throughput technologies for drug and genetic screens enable genome-wide screen for genes sensitizing drugs. However, testing all possible combinations of hundreds of cell lines and thousands of compounds is infeasible and unaffordable in the foreseen future. Therefore, development of high performance classifiers that can effectively predict which genes sensitize which drugs for a given cell line will significantly reduce the number of experiments and thus greatly shorten the cycle of developing effective therapeutics.
Autonomous and Real-time Classification/Prediction Systems for Diagnosis and Treatments (ARCPS)
Enormous data from each single patient is being generated but it remains challenging how to make best use of the information for personalized medicine. ARCPS will take as inputs all pathological, clinical, genetic, genomic, proteomic, and metabolic information to classify patients, predict disease progression, determine drug response, and decide optimal treatments. Given the multi-modal nature of the input data, those complex high-dimension data types such as image, DNA, mRNA, protein and sequencing need go through different feature extractors to yield meaningful features for training and classification/prediction.Reconstruction and Analysis of Multiscale Biological Networks
Advanced algorithms for reconstructing and analyzing multiscale biological networks are being developed to effectively and efficiently uncover novel targets, pathways and mechanisms driving complex human diseases including cancer, obesity, diabetes, cardiovascular and neurodegenerative disease. These data-driven drivers and pathways can be used to establish global driver-disease and pathway-disease connectivity maps that will be further utilized to develop testable hypotheses for laboratory and/or clinical validations.Publications
Tran LM, Zhang B. Inferring causal genomic alterations in breast cancer using gene expression data. BMC systems biology 2011; 5.
Greenawalt DM, Dobrin R, Chudin E, Hatoum IJ, Suver C, Beaulaurier J, Zhang B, Castro V, Zhu J, Lum PY, Schadt EE, Kaplan LM. A survey of the genetics of stomach, liver, and adipose gene expression from a morbidly obese cohort. Genome research 2011 Jul; 21(7).
Fraser HB, Babak T, Tsang J, Zhou Y, Zhang B, Mehrabian M, Schadt EE, He A, Truong A, Patel S, Nelson SF, Horvath S, Berliner JA, Kirchgessner TG, Lusis AJ, Lum PY, Schadt EE, Kaplan LM. Systematic detection of polygenic cis-regulatory evolution. PLoS genetics 2011 Mar; 7(3).
Zhang H, Meng F, Liu G, Zhang B, Zhu J, Wu F, Ethier SP, Miller F, Wu G, Patel S, Nelson SF, Horvath S, Berliner JA, Kirchgessner TG, Lusis AJ. Forkhead transcription factor foxq1 promotes epithelial-mesenchymal transition and breast cancer metastasis. Cancer research 2011 Feb; 71(4).
Pandey G, Zhang B, Chang AN, Myers CL, Zhu J, Kumar V, Schadt EE, Miller F, Wu G. An integrative multi-network and multi-classifier approach to predict genetic interactions. PLoS computational biology 2010; 6(9).
Yang X, Zhang B, Molony C, Chudin E, Hao K, Zhu J, Gaedigk A, Suver C, Zhong H, Leeder JS, Guengerich FP, Strom SC, Schuetz E, Rushmore TH, Ulrich RG, Slatter JG, Schadt EE, Kasarskis A, Lum PY. Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. Genome research 2010 Aug; 20(8).
Millstein J, Zhang B, Zhu J, Schadt EE, Hao K, Zhu J, Gaedigk A, Suver C, Zhong H, Leeder JS, Guengerich FP, Strom SC, Schuetz E, Rushmore TH, Ulrich RG, Slatter JG, Schadt EE, Kasarskis A, Lum PY. Disentangling molecular relationships with a causal inference test. BMC genetics 2009; 10.
Schadt EE, Zhang B, Zhu J, Schadt EE. Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments. Genetica 2009 Jun; 136(2).
Zhu J, Zhang B, Schadt EE. A systems biology approach to drug discovery. Advances in genetics 2008; 60.
Zhu J, Zhang B, Smith EN. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nature genetics 2008 Jul; 40(7).
Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, Carlson S, Helgason A, Stefansson H, Fossdal R, Kristjansson K, Gislason HG, Stefansson T, Leifsson BG, Thorsteinsdottir U, Lamb JR, Gulcher JR, Reitman ML, Kong A, Schadt EE, Stefansson K. Genetics of gene expression and its effect on disease. Nature 2008 Mar; 452(7186).
Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ, Zhang C, Lamb J, Edwards S, Sieberts SK, Leonardson A, Castellini LW, Wang S, Champy MF, Zhang B, Emilsson V, Doss S, Ghazalpour A, Horvath S, Drake TA, Lusis AJ, Schadt EE, Stefansson H, Fossdal R, Kristjansson K, Gislason HG, Stefansson T, Leifsson BG, Thorsteinsdottir U, Lamb JR, Gulcher JR, Reitman ML, Kong A, Schadt EE, Stefansson K. Variations in DNA elucidate molecular networks that cause disease. Nature 2008 Mar; 452(7186).
Langfelder P, Zhang B, Horvath S, Yang X, Pinto S, MacNeil DJ, Zhang C, Lamb J, Edwards S, Sieberts SK, Leonardson A, Castellini LW, Wang S, Champy MF, Zhang B, Emilsson V, Doss S, Ghazalpour A, Horvath S, Drake TA, Lusis AJ, Schadt EE. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics (Oxford, England) 2008 Mar; 24(5).
Horvath S, Zhang B, Carlson M, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proceedings of the National Academy of Sciences of the United States of America 2006 Nov; 103(46).
Gargalovic PS, Imura M, Zhang B, Gharavi NM, Clark MJ, Pagnon J, Yang WP, He A, Truong A, Patel S, Nelson SF, Horvath S, Berliner JA, Kirchgessner TG, Lusis AJ, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS. Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids. Proceedings of the National Academy of Sciences of the United States of America 2006 Aug; 103(34).
Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R, Brozell A, Schadt EE, Drake TA, Lusis AJ, Horvath S, Horvath S, Berliner JA, Kirchgessner TG, Lusis AJ. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS genetics 2006 Aug; 2(8).
Carlson MR, Zhang B, Fang Z, Mischel PS, Horvath S, Nelson SF, Brozell A, Schadt EE, Drake TA, Lusis AJ, Horvath S. Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks. BMC genomics 2006; 7.
Zhang B, Horvath S, Fang Z, Mischel PS, Horvath S, Nelson SF. A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology 2005; 4.
Zhang B, Horvath S. Ridge regression based hybrid genetic algorithms for multi-locus quantitative trait mapping. International journal of bioinformatics research and applications 2005; 1(3).
Zhang B, Srihari S. Fast k-nearest Neighbor Classification Using Cluster-based Trees. IEEE Transaction Pattern Analysis and Machine Intelligence 2004; 26(4).
Industry Relationships
Physicians and scientists on the faculty of the Icahn School of Medicine at Mount Sinai often interact with pharmaceutical, device and biotechnology companies to improve patient care, develop new therapies and achieve scientific breakthroughs. In order to promote an ethical and transparent environment for conducting research, providing clinical care and teaching, Mount Sinai requires that salaried faculty inform the School of their relationships with such companies.
Below are financial relationships with industry reported by Dr. Zhang during 2012 and/or 2013. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.
Consulting:
- Genomic Health, Inc.
Mount Sinai's faculty policies relating to faculty collaboration with industry are posted on our website at http://icahn.mssm.edu/about-us/services-and-resources/faculty-resources/handbooks-and-policies/faculty-handbook. Patients may wish to ask their physician about the activities they perform for companies.
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