Bio


Zhi Huang received his Bachelor of Science degree in Automation (BS--MS straight entrance class) from Xi'an Jiaotong University School of Electronic and Information Engineering in June 2015. In August 2021, He received a Ph.D. degree from Purdue University, majoring in Electrical and Computer Engineering (ECE).
His background is in the area of Machine and Deep Learning, Computational Pathology, Computational Biology, and Bioinformatics.
From May 2019 to August 2019, he was at Philips Research North America as a Research Intern.

Professional Education


  • Doctor of Philosophy, Purdue University (2021)
  • Master of Science, Indiana-Purdue University Indianapolis (2016)
  • Bachelor of Science, Xi'An Jiaotong University (2015)

All Publications


  • Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data. Nature medicine Liu, R., Rizzo, S., Waliany, S., Garmhausen, M. R., Pal, N., Huang, Z., Chaudhary, N., Wang, L., Harbron, C., Neal, J., Copping, R., Zou, J. 2022

    Abstract

    Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation-mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.

    View details for DOI 10.1038/s41591-022-01873-5

    View details for PubMedID 35773542

  • TSUNAMI: Translational Bioinformatics Tool Suite for Network Analysis and Mining GENOMICS PROTEOMICS & BIOINFORMATICS Huang, Z., Han, Z., Wang, T., Shao, W., Xiang, S., Salama, P., Rizkalla, M., Huang, K., Zhang, J. 2021; 19 (6): 1023-1031

    Abstract

    Gene co-expression network (GCN) mining identifies gene modules with highly correlated expression profiles across samples/conditions. It enables researchers to discover latent gene/molecule interactions, identify novel gene functions, and extract molecular features from certain disease/condition groups, thus helping to identify disease biomarkers. However, there lacks an easy-to-use tool package for users to mine GCN modules that are relatively small in size with tightly connected genes that can be convenient for downstream gene set enrichment analysis, as well as modules that may share common members. To address this need, we developed an online GCN mining tool package: TSUNAMI (Tools SUite for Network Analysis and MIning). TSUNAMI incorporates our state-of-the-art lmQCM algorithm to mine GCN modules for both public and user-input data (microarray, RNA-seq, or any other numerical omics data), and then performs downstream gene set enrichment analysis for the identified modules. It has several features and advantages: 1) a user-friendly interface and real-time co-expression network mining through a web server; 2) direct access and search of NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, as well as user-input gene expression matrices for GCN module mining; 3) multiple co-expression analysis tools to choose from, all of which are highly flexible in regards to parameter selection options; 4) identified GCN modules are summarized to eigengenes, which are convenient for users to check their correlation with other clinical traits; 5) integrated downstream Enrichr enrichment analysis and links to other gene set enrichment tools; and 6) visualization of gene loci by Circos plot in any step of the process. The web service is freely accessible through URL: https://biolearns.medicine.iu.edu/. Source code is available at https://github.com/huangzhii/TSUNAMI/.

    View details for DOI 10.1016/j.gpb.2019.05.006

    View details for Web of Science ID 000847852700013

    View details for PubMedID 33705981

    View details for PubMedCentralID PMC9403021