We established “BiOmics Laboratory” which emphasizes integrating Biological Big Data Analysis and Deep Learning Computation. The primary focuses include bioinformatics, genomics and proteomics, network biology, Pan-cancer Omics analysis, single-cell sequencing and spatial transcriptomics, AI-driven drug design, biological database design, and software development covering a wide range of topics including cancer biomarkers, pathogen drug resistance analysis, peptide drug design, systems biology, and the application of machine learning and deep learning in smart healthcare. Our research team currently specializes in protein post-translational modifications (PTMs), protein ubiquitylation and E3 ligases regulations, antimicrobial peptides (AMPs), multidrug-resistant pathogens diagnosis, omics-based pan-cancer analysis, spatial transcriptomics, and AI-driven drug design.

The BiOmics Laboratory collaborates with multiple hospitals and medical centers to conduct personalized medical testing for patients. One notable partnership is with the Department of Laboratory Medicine at Linkou Chang Gung Memorial Hospital, where we have developed pathogen detection technologies. By integrating machine learning methods with high-resolution protein mass spectrometry (MALDI-TOF MS), we can identify common hospital pathogens (such as Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Streptococcus pneumoniae, and Enterococcus faecium) with an accuracy exceeding 98%, surpassing traditional blood culture detection methods. We collected over one hundred thousand records of patients' pathogen antibiotic susceptibility test data and published over 10 articles on pathogen antibiotic resistance testing in top international journals. Additionally, we have developed a [rapid detection technology for pathogen resistance based on high-resolution mass spectrometry], which was granted a patent in 2021 by Taiwan, and two patents, 2022 and 2023, by the United States.

Over the past few years, our research team has also integrated big data analysis and deep learning methods to develop various systems biology approaches and analysis platforms for multi-omics, single-cell sequencing, and spatial transcriptome data. Our research team not only made new progress in the study of human protein kinases and related phosphorylation sites, including evolutionary studies of protein kinases and phosphorylation substrate specificity analysis, but we have also actively collaborated with Professor Alexander G. Marneros from Harvard Medical School and Professor Yun-Xin Fu and Professor John Shyy from UCSD on phosphoproteomics-related research. The results have been published in PNAS (SCI IF: 12.777), Cell Reports (SCI IF: 9.995), and Circulation (SCI IF: 37.8).