Kaohsiung Medical University

Bioinformatics in Multi-omics Data Analysis Lab

BMDA Lab builds computational methods that integrate genomic, transcriptomic, proteomic, and lipidomic data to advance cancer research.

Meet the Team View Publications

Members

Chia-Hsin (Martin) Liu, Ph.D.

Principal Investigator, BMDA Lab

Assistant Professor, Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University

Chia-Hsin Liu is a bioinformatician whose work sits at the boundary of biology, statistics, and data science, building methods and software that turn large-scale omics data into usable insight for cancer research. He earned his B.S. and M.S. in Life Science at National Tsing Hua University before completing a Ph.D. in Bioinformatics through the Taiwan International Graduate Program at the Institute of Statistical Science.

In 2021 he joined Cancer Biology and Precision Therapeutics Center at China Medical University as a postdoctoral researcher, where he led the development of several multi-omics web platforms used by the wider cancer research community — including DriverDBv4, LipidSig 2.0, and LipidSigR. In 2025 he joined Kaohsiung Medical University as an Assistant Professor in the Graduate Institute of Clinical Medicine, College of Medicine, where he now leads the BMDA Lab.

ORCID Profile ↗ Web of Science ↗

2007

Research Assistant

Institute of Statistical Science, Academia Sinica

2008 – 2016

Ph.D., Bioinformatics

Taiwan International Graduate Program, National Yang-Ming University & Academia Sinica

2016 – 2018

Postdoctoral Research

Institute of Statistical Science, Academia Sinica — immuno-oncology & machine learning

2018 – 2020

R&D Lead, Biomedical Division

Acer Inc.

2021 – 2025

Postdoctoral Researcher

Cancer Biology and Precision Therapeutics Center, China Medical University

2025 – Present

Assistant Professor

Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University

We're Recruiting

The BMDA Lab is currently building its team at Kaohsiung Medical University. We welcome graduate students and research assistants interested in cancer research, lipidomics data analysis, and hypoxia study to get in touch.

What We Do

BMDA Lab develops computational and statistical methods for integrating multi-omics data — genomic, transcriptomic, proteomic, and lipidomic — and applies them to cancer biomarker discovery, combining rigorous statistical modeling with practical, browser-based tools that experimental and clinical collaborators can use directly.

Multi-Omics Data Integration

Statistical and machine-learning methods that combine genomic, transcriptomic, proteomic, and lipidomic data into unified models of cancer biology.

Cancer Biomarker Discovery

Mining large-scale multi-omics cohorts to identify and validate candidate biomarkers with a clear path toward clinical application.

Lipidomics & Metabolic Omics

Extending multi-omics integration beyond the genome-to-proteome axis into metabolite and lipid data.

Immuno-Oncology

Studying tumor–immune interactions and immune-checkpoint biology through large-scale sequencing data.

Biomedical Statistics & Machine Learning

Applying rigorous statistical modeling and machine-learning methods to high-dimensional biological data.

Research Computing Infrastructure

Designing and maintaining the Linux server systems and databases that power the group's multi-omics web platforms.

Because most existing integration methods focus on the genome-to-proteome axis defined by the central dogma, layers such as the metabolome and lipidome remain comparatively underexplored. Building on the lab's experience in lipidomics, we aim to extend integrative frameworks to incorporate these layers, and to develop biomarker candidates in close collaboration with oncology clinicians so that clinical relevance shapes the research from its earliest stages. Mature methods are packaged as open-source software — released through repositories such as CRAN and Bioconductor — and as web-based tools and databases for the wider research community.

Selected Publications

A selection of his work is listed below. For the complete, continuously updated record, see his ORCID profile and Web of Science profile.

As First Author

Liu C-H, Shen P-C, Tsai M-H, Liu H-C, Lin W-J, Lai Y-L, Wang Y-D, Hung M-C, Cheng W-C. LipidSigR: a R-based solution for integrated lipidomics data analysis and visualization. Bioinformatics Advances, 2025.
Liu C-H, Shen P-C, Lin W-J, Liu H-C, Tsai M-H, Huang T-Y, Chen I-C, Lai Y-L, Wang Y-D, Hung M-C, Cheng W-C. LipidSig 2.0: integrating lipid characteristic insights into advanced lipidomics data analysis. Nucleic Acids Research, 2024. IF 16.6
Liu C-H, Lai Y-L, Shen P-C, Liu H-C, Tsai M-H, Wang Y-D, Lin W-J, Chen F-H, Li C-Y, Wang S-C, Hung M-C, Cheng W-C. DriverDBv4: a multi-omics integration database for cancer driver gene research. Nucleic Acids Research, 2024. IF 16.6

Selected Co-Authored Papers

Lin W-J, Wang Y-D, Liu C-H, et al. ImmiR: a database of microRNAs associated with immune checkpoints and infiltrates. Computational and Structural Biotechnology Journal, 2025.
Lin W-J, Liu C-H, Hung M-S, et al. LipidFun: a database of lipid functions. Bioinformatics, 2025.
Lin Y-Z, Liu C-H, Wu W-R, et al. Memory-promoting function of miR-379-5p attenuates CD8+ T cell exhaustion by targeting immune checkpoints. Journal for ImmunoTherapy of Cancer, 2025.
Widjaya MA, Liu C-H, Lee S-D, Cheng W-C. Transcriptomics meta-analysis reveals phagosome and innate immune system dysfunction as potential mechanisms in the cortex of Alzheimer's disease mouse strains. Journal of Molecular Neuroscience, 2023.
Lai Y-L, Liu C-H, Wang S-C, et al. Identification of a steroid hormone-associated gene signature predicting the prognosis of prostate cancer through an integrative bioinformatics analysis. Cancers, 2022.