We, as computer scientists, are interested in bioinformatics, systems biology, and computational biology. Bioinformatics uses mathematics, statistics, computer, and engineering to develop algorithms and software programs to understand and analyze biological data.

Managing the various types of complex biological data is an immense challenge and a great research opportunity. Therefore, my main research area is pattern recognition in bioinformatics, especially, biological motif analysis.

We developed various bioinformatics tools online, and you can find the various programs and links from this page. 

• NemoSuite: Network motif detection program with network-centric and motif-centric approaches
• Web-QuateXelero: Web-based QuateXelero program (Under Construction)  
• LightBox: Interpretable Machine Learning program (testing, UW internal only)
•  Timeseries data clustering(testing, UW internal only)   

Bioinformatics work with various dataset. Here are some list of public dataset.

• Bioinformatics related dataset: Links to public datasets
• Public Bioinformatics Datasets: Include Genomics, Transcriptomics, Epigenomics, Clinical and Healthcare
• MD Anderson Cancer Centa Public Datasets: Include Standardized TCGA Data
• Collection of bioinformatics datasets on Kaggle: Include Standardized TCGA Data

To visit the PI's main homepage, please visit the homepage page.

Contact

Wooyoung Kim, Ph.D.,

Email: kimw6[at]uw.edu