Welcome to Computational Biology Lab

  • Incomplete Time-Series Gene Expression in Integrative Study for Islet Autoimmunity Prediction (Briefings in Bioinformatics, 2022)

Our lab is interested in developing sophisticated machine learning approaches to extract useful information from the large-scale multi-omics data to understand the complex disease such as cancer. Our research covers several important topics in cancer transcriptome, spanning from technique-driven research that aims at developing graph-based learning models for cancer transcriptome analysis with prior knowledge (e.g., isoform quantification, biomarker identification, cancer outcome prediction, drug sensitivity prediction), to hypothesis-driven investigation of specific biological problems (e.g., changes of transcriptome upon mTOR hyper-activation). Our development leads to novel computational models and molecular signatures, which could be used in early detection, diagnosis, and prognosis of specific tumors.

List of Projects