SUMMARY
Shulan Tian, Ph.D., M.S., is a bioinformatician with an interdisciplinary background in the areas of genomics, bioinformatics and data science. She conducts research focusing on translational informatics that leverages the power of population genomics, electronic health records and machine learning to catalyze clinically impactful discovery. Dr. Tian collaborates comprehensively with basic science and clinical investigators to advance the molecular understanding of a variety of complex diseases, including steatotic liver disease, neurodegenerative disorders, obesity, osteoarthritis, and brain and blood cancers. Her long-term research goals involve empowering genomic medicine by developing state-of-the-art machine learning approaches for multi-omics data integration.
Focus areas
- Large-scale genomic data mining. Dr. Tian develops predictive analytics to accelerate translational discovery by combining population genomics and electronic health record data. She currently leads bioinformatics efforts for the Tapestry DNA Sequencing Research Study, a population genomics initiative that seeks to advance research and improve people's care by sequencing 100,000 consented Mayo Clinic participants.
- Translational research in complex disease. Dr. Tian investigates how genetic factors contribute to complex diseases such as steatotic liver disease. She is particularly interested in developing new machine learning-based deep phenotyping that improves genomic discovery and enables stratification for individualized medicine.
- Cancer bioinformatics. Dr. Tian develops original computational methods to effectively detect somatic mutations in tumor- or blood-based genomic profiling. These computational methods detect low-level somatic mutations, which prove difficult but critical for developing targeted therapy and malignancy risk predictions.
- Long-read sequencing data analysis. Dr. Tian actively involves genomics studies in neurodegenerative disorders using long-read sequencing technology. She develops long-read bioinformatics pipelines to identify genes that may account for the underlying clinicopathological variability in amyotrophic lateral sclerosis and frontotemporal dementia.
Significance to patient care
Dr. Tian's translational bioinformatics research in developing genomic data analytics will ultimately support individualized care in the clinic. By integrating genomics, proteomics, metabolomics and electronic medical record data, Dr. Tian aims to develop a new analytic framework for disease subtyping using machine learning-based techniques. Disease subtyping helps in customizing the design of genetic testing, improving clinical trials for complex diseases and allowing better understanding of the causes of diseases for clinical practice. Additionally, with her interests in cancer genomic profiling, Dr. Tian strives to build a bioinformatics roadmap to advance targeted therapy selection.
Professional highlights
- Section editor, Journal of Medical Internet Research Bioinformatics and Biotechnology, 2023-present.
- Associate editor, Frontiers in Oncology, 2021-present.
- Topic editor, Frontiers in Genetics, 2020-2023.
- Reviewers' choice abstract winner, American Society of Human Genetics Annual Conference, 2015, 2016, 2019, 2022.
- Data Science Mentorship Award, Center for Individualized Medicine, Mayo Clinic, 2020.