Research Mission

The aim of our research is to leverage cutting-edge computational methods for analyzing large-scale biological datasets, with the ultimate objective of enhancing our comprehension of human biology and disease. To this end, we seek to integrate genomic, transcriptomic, proteomic, epigenomic, and metabolomic data at both bulk and single-cell levels in order to identify novel therapeutic targets and biomarkers that hold promise for improving patient outcomes in the context of precision medicine.

Research Areas

Advanced Machine Learning for Sequencing Data in Human Biology

We apply advanced machine learning algorithms to analyze high-throughput sequencing datasets in human biology. Our work enables us to better understand the complex interplay between genetic variation, gene expression, and biological function, including the identification of novel genes implicated in diseases like cancer.

Protein Subcellular Location Prediction for Disease Pathogenesis

Our research utilizes computational methods such as machine learning techniques to develop accurate predictive models of protein subcellular locations. This work helps us to better understand the roles that proteins play within cells and in the progression of various diseases, ultimately leading to the identification of novel drug targets and therapies.

Bioinformatic Analysis for Therapeutic Targets and Biomarkers

Our work in this area focuses on the use of bioinformatic analysis to identify potential therapeutic targets and biomarkers that can be used to optimize treatment strategies. By analyzing complex biological datasets, including clinical and experimental disease data, we can uncover diagnostic and prognostic indicators of disease that help clinicians make more informed decisions about patient care.

Novel Bioinformatics Methods for Complex Biological Problems

Biological systems are inherently complex, often involving interactions among multiple types of molecules and processes. Our research in this area involves the development of novel bioinformatics methods that can address these challenges. This may include the development of new computational algorithms or machine learning models that provide insight into biological systems that are difficult to study using traditional experimental methods.

Overall, our research is grounded in the belief that technological advancements in sequencing, proteomics, and other large-scale biological datasets can transform our understanding of human biology and disease, ultimately leading to better patient outcomes in precision medicine. We are grateful for your interest in our research program and look forward to making continued progress in the future.