We, as computer scientists, are interested in bioinformatics, systems biology, and computational biology. Bioinformatics integrates biology, chemistry, mathematics, statistics, computer science, and engineering to develop algorithms and software for understanding and analyzing biological data.
Managing the many types of complex biological data presents both immense challenges and significant research opportunities. Accordingly, our primary research focus is pattern recognition in bioinformatics, with particular emphasis on biological motif analysis and interpretable bioinformatics machine learning (iBioML).
Biological motifs are recurring patterns that are presumed to have important biological significance. When identified in DNA, RNA, or protein sequences, they are referred to as sequence motifs; when identified in networks, they are known as network motifs. Motifs have been widely used to address real-world problems in biology and medicine. Currently, our work focuses on network motif analysis within the context of systems biology. More details about our research on network motifs can be found on the Research page.
Interpretable Bioinformatics Machine Learning (iBioML) aims to bridge the gap between predictive performance and biological insight. While modern machine learning models can achieve high accuracy, their black-box nature often limits their usefulness in scientific discovery and clinical decision-making. Our research in iBioML focuses on developing machine learning models that are not only accurate and robust but also biologically interpretable. By embedding domain knowledge, motif-based features, and transparent model structures, we seek to enable researchers and practitioners to better understand the underlying biological mechanisms revealed by data-driven models.
As bioinformatics is inherently multidisciplinary, we collaborate closely with biologists and medical practitioners. Our ongoing projects include detecting evolutionary processes in genome sequencing data and developing data visualization methods for immunotherapy research. If you are interested in these projects, please visit the Research page.
|
