Dr. Huang’s research focuses on 1. m6A methylation and its role in cancer. His lab uses a combination of computation/AI and high throughput profiling technologies to 1) delineate regulation of m6A deposition; 2) determine the mechanisms by which m6A regulates gene expression and downstream functions; 3) m6A’s role in cancer and viral infection. His lab developed many computation tools and resources for analyzing m6A profiling data and predicting m6A functions including the exomePeak pipeline for detecting m6A and differential m6A sites from MeRIP-seq, m6A-express for predicting m6A-regulation of gene expression, FunDMDeep-m6A for prioritizing functional differential m6A sites, and the MeT-DB database.
2. AI for precision oncology.
Develop novel deep learning and AI models that can 1) perform cancer phenotype predictions and, at the same time, 2) identifying markers and generate explainable mechanisms. His lab has developed several genomics-based deep learning/AI tools for cancer prognosis and survival analysis, drug response prediction, and cancer gene dependence prediction.
- MS, Electrical Engineering, Stony Brook University, 1997
- PhD, Electrical Engineering, Stony Brook University, 2001
Education & Training
M Flores, Z Liu, TH Zhang, MM Hasib, YC Chiu, Z Ye, K Paniagua, S Jo, J. Zhang, S-J Gao, Y. Chen*, Y. Huang*. Deep learning tackles single-cell analysis A survey of deep learning for scRNA-seq analysis. arXiv preprint arXiv:2109.12404. 2021; *:corresponding_author.
Zhang, T., Zhang, S. W., Zhang, S. Y., Gao, S. J., Chen, Y.*, & Huang, Y.*. m6Aexpress: uncovering complex and condition-specific m6A regulation of gene expression. Nucleic Acids Research. 2021; gkab714
Y-C Chiu, S. Zheng, L-J Wang, B. S. Iskra, M. K. Rao, P. J. Houghton, Y. Huang*, Y. Chen*. Predicting and characterizing a cancer dependency map of tumors with deep learning. Science Advances. 2021; 7 (34), eabh1275
Mostavi, M., Chiu, Y.C., Chen, Y.* and Huang, Y.*. CancerSiamese: one-shot learning for predicting primary and metastatic tumor types unseen during model training. BMC bioinformatics. 2021; 22(1), pp.1-17.
Chiu YC, Chen HI, Gorthi A, Mostavi M, Zheng S, Huang Y*, Chen Y*. Deep learning of pharmacogenomics resources: moving towards precision oncology. Briefings in Bioinformatics. 2020; bbz144, https://doi.org/10.1093/bib/bbz144
Gruffaz, M., Zhang, T., Marshall, V., Goncalves, P., Ramaswami, R., Labo, N., Whitby, D., Uldrick, T, T. S., Yarchoan, R., Huang, Y., & Gao, S.-J.*. Signatures of oral microbiome in HIV-infected individuals with oral Kaposi's sarcoma and cell-associated KSHV DNA. PLOS PATHOGENS. 2020; 16(1)
Chiu YC, Chen HH, Zhang T, Zhang S, Gorthi A, Wang LJ, Huang Y, Chen Y. Predicting drug response of tumors from integrated genomic profiles by deep neural networks. BMC Med Genomics. 2019; 31;12(Suppl 1):18
Panneerdoss S, Eedunuri VE, Timilsina S, Rajamanickam S, Suryavathi V, Abdelfattah S, Onyeagucha BC,, Cui X, Mohammad TA, Huang THM, Huang Y*, Chen Y*, Rao MK*. Cross-talk among writers, readers, and erasers of m6A regulates cancer growth and progression. Science Advances. 2018; 4(10)
Tan, B., Liu, H., Zhang, S., da Silva, S. R., Zhang, L., Meng, J., Cui, X., Yuan, H., Sorel, O., Zhang, S., Huang*, Y., Gao*, S-J. Viral and Cellular N6-Methyladenosine (m6A) and N6, 2'-ODimethyladenosine (m6Am) Epitranscriptomes in KSHV Life Cycle. Nature Microbiology. 2018; 3(1): 108-120.
Liu H, Wang H, Wei Z, Zhang S, Hua G, Zhang S, Zhang L, Gao S-J, Meng* J, Chen* X, Huang Y*. Elucidating context-specific functions of N6-methyl-adenosine methyltranscriptome. Nucleic Acids Research. 2018; 4;46(D1): D281-D287.
- NSF Career Award, 2005
- Best Paper Award, IEEE Signal Processing Magazine, 2007
- UTSA Presidential Achievement Award in Research, 2018
- Distinguished Service Award, International Association of Intelligent Biology and Medicine, 2019
