Yonghua Zhuang, PhD
Assistant Professor, Pediatrics-Endocrinology
Recognition & Awards
Strother Walker Award for Outstanding PhD Student, University of Colorado Anschutz Medical Campus
NHLBI BioData Catalyst Fellowship, NHLBI
My research interests are the development and application of statistical methods for analyzing high throughput omics data to better understand how key interactions between genes (or proteins) contribute to human diseases. My academic training and research experience have provided me with a strong integrated background in biostatistics, machine learning, and biology. I have received interdisciplinary training including immunology, molecular biology, statistics methods in genomics, network inference, bioinformatics, and deep learning. Recently I focuses on developing statistical methods for constructing protein-protein networks associated with phenotype and developing Graph Convolutional Neural Network algorithms in the field of genomics for classification. To date, my colleagues and I have developed several statistical methods including a novel tissue augmented Bayesian model for eQTL analysis, sparse multiple canonical correlation network analysis (smCCNet), and an augmented high-dimensional graphical Lasso method to incorporate prior biological knowledge for global network learning (ahGlasso).
Abdel-Hafiz M, Najafi M, Helmi S, Pratte KA, Zhuang Y, Liu W, Kechris KJ, Bowler RP, Lange L, Banaei-Kashani F. Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification. Front Big Data. 2022;5:894632. PubMed PMID: 35811829
Zhuang Y, Xing F, Ghosh D, Banaei-Kashani F, Bowler RP, Kechris K. An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning. Front Genet. 2022 Jan 27;12:760299. doi: 10.3389/fgene.2021.760299. PMID: 35154240; PMCID: PMC8829118.
Zhuang Y, Xing F, Ghosh D, Banaei-Kashani F, Bowler RP, Kechris K. An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning. Front Genet. 2021;12:760299. PubMed PMID: 35154240
Zhuang Y, Hobbs BD, Hersh CP, Kechris K. Identifying miRNA-mRNA Networks Associated With COPD Phenotypes. Front Genet. 2021;12:748356. PubMed PMID: 34777474
Gillenwater LA, Helmi S, Stene E, Pratte KA, Zhuang Y, Schuyler RP, Lange L, Castaldi PJ, Hersh CP, Banaei-Kashani F, Bowler RP, Kechris KJ. Multi-omics subtyping pipeline for chronic obstructive pulmonary disease. PLoS One. 2021;16(8):e0255337. PubMed PMID: 34432807