Research Interests

My research focuses on understanding complex human diseases by undertaking integrative approaches, which combine biology, computational and statistical learning, bioinformatics, and genomics. In particular, I am interested in developing and applying computational algorithms, software and tools:

• to help identify genetic and regulatory mechanisms underlying human diseases, so that we can better understand why different genetic and gene expression changes in patients can lead to different disease phenotypes;
• to identify biomarkers, and classify and diagnose various human diseases using metadata (e.g., genetic, gene expression and phenotypic data); and
• to predict outcomes of patients with human diseases using metadata.

My current work involves:

• phenotyping asthma patients using computational approaches;
• understanding regulatory mechanisms of different subtypes of asthma using systems biology and machine learning approaches;
• transcriptome-wide screening of expression traits and molecular markers for drug abuse using machine learning approach;
• genome-wide discovery of new drug targets for drug abuse and their epistatic genetic influences via structured association mapping.

Selected Publications

• Wang H, Oscar Lopez, Wu W*, Xing EP* (2022) Gene Set Prioritization Guided by Regulatory Networks with p-Values Through Kernel Mixed Model. The 26th International Conference on Research in Computational Molecular Biology (RECOMB) *co-corresponding authors
• Wu W (2020) Predicting atopic asthma by using eNose breath profiles with machine learning. (Editorial), J Allergy Clin Immunol., 146(5):1010-1012.
• Wu W* et al (2019) Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma, Am J Respir Crit Care Med., 199(11):1358-1367. *Wu W is the co-senior author on this paper.
• Wu X, Xie S, Wang L, Fan P, Ge S, Xie XQ, Wu W (2018) A computational strategy for finding novel targets and therapeutic compounds for opioid dependence, PLoS One. 13(11):e0207027.
• Wu W* et al (2014) Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data. J Allergy Clin Immunol. 133(5):1280-1288. *Wu W is the co-senior author on this paper.
• Parikh AP*, Wu W*, Curtis R, Xing EP (2011) Reverse Engineering Tree-Evolving Gene Networks Underlying Developing Biological Lineages. Proceedings of the Nineteenth International Conference on Intelligence Systems for Molecular Biology (ISMB). Bioinformatics 27(13): i196-i204. Recipient of the ISMB BEST PAPER Award. *Wu W is the co-first author on this paper.

In the news:

• March 2014: CMU press release about the asthma phenotyping project by using machine learning approaches - joint work with Dr. Sally Wenzel at UPMC and the SARP team. Also in:
  • CMU Homepage Stories
  • Pittsburgh's NPR News Station, WESA 90.5
  • Pittsburgh Post-Gazette
  • Biomedical Beat, National Institute of General Medical Sciences, NIH
• July 2014: CMU press release about breast cancer work by using dynamic network approach - joint work with Dr. Eric Xing's lab at CMU and Dr. Mina Bissell's lab at Lawrence Berkeley National Lab.