Jingwen Yan, Ph.D.
- Assistant Professor, Bioinformatics
Contactjingyaniupui [dot] edu
- Ph.D. Bioinformatics, Indiana University (2015)
Jingwen Yan’s work is focused on integrating data sources to extract knowledge that can lead to better understanding of complex diseases, neurological conditions, and dementias, such as Alzheimer’s disease
In 2018 she was awarded a CISE Research Initiation Initiative (CRII) grant by the National Science Foundation for her work in developing a novel, data-intensive computational framework to analyze and integrate data and networks, create predictive models, and reveal their associations. This work could lead to more efficient discovery of disease markers for designing new drugs and treatment protocols.
Before joining the Department of BioHealth Informatics at SOIC, Yan worked as a research scientist at Indiana University Network Science Institute from 2015 to 2016.
Her research focuses on exploring genotype-phenotype associations by developing efficient and scalable computational and bioinformatics approaches. Specifically, Yan is interested in the combination of machine learning and network science such that rich biological knowledge can be properly incorporated to guide the learning procedure.
Her methods have been applied to integrative analysis of high throughput multi-omic data, multimodal neuroimaging data, fluid and cognitive biomarker data, and rich biological knowledge including pathways and networks, with applications to various neurodegenerative disorders.
She is a member of Center for Computational Biology and Bioinformatics and also is associated with Center for Neuroimaging and Network Science Institute at Indiana University.
- Machine learning
- Network science
- Genotype-phenotype association
- Multi-omics integration
- Biomarker discovery
- Systems biology
INFO B419 Introduction to Bioinformatics
INFO B519 Introduction to Bioinformatics
INFO B473 Application Programming for Biomedical Data Analysis
INFO B573 Programming for Science Informatics
- July 25, 2018
Yan receives NSF grant to develop novel tools for mining complex biological data
- January 27, 2017
School welcomes 12 new faculty members