Bin Zhang, PhD
img_Bin Zhang
PROFESSOR | Genetics and Genomic Sciences
PROFESSOR | Pharmacological Sciences
Research Topics
Adipose, Aging, Allergy, Alzheimer's Disease, Anti-Tumor Therapy, Apoptosis/Cell Death, Autism, Autophagy, Axonal Growth and Degeneration, Bioinformatics, Bone Biology, Bone Metabolism, Brain, Cancer, Cancer Genetics, Cell Cycle, Cerebral Cortex, Cognitive Neuroscience, Computational Neuroscience, Diabetes, Epigenetics, Gene Discovery, Gene Expressions, Gene Regulation, Gene Therapy, Genetics, Genetics of Movement disorders, Genomics, Glutamate (NMDA & AMPA) Receptors, Glutathione, Hippocampus, Human Genetics and Genetic Disorders, Image Analysis, Immunology, Infectious Disease, Inflammation, Liver, Lung, Mathematical Modeling of Biomedical Systems, Mathematical and Computational Biology, Memory, Metastasis, Microarray, Microglia, Mitosis, Molecular Biology, Motor Control, Obesity, Oncogenes, Prefrontal Cortex, Protein Complexes, Protein Folding, RNA Splicing & Processing, Tumor Suppressor Genes, Tumorigenesis
Multi-Disciplinary Training Area
Genetics and Genomic Sciences [GGS], Neuroscience [NEU]
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.


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.


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.

BE, Tongji University

MS, State University of New York at Buffalo

MS, Tsinghua University

PhD, State University of New York at Buffalo

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.

Dr. Zhang did not report having any of the following types of financial relationships with industry during 2022 and/or 2023: consulting, scientific advisory board, industry-sponsored lectures, service on Board of Directors, participation on industry-sponsored committees, equity ownership valued at greater than 5% of a publicly traded company or any value in a privately held company. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.

Mount Sinai's faculty policies relating to faculty collaboration with industry are posted on our website. Patients may wish to ask their physician about the activities they perform for companies.