Yidan Pan
Associate Scientist, Postdoctoral Fellow, Merck
I am an associate scientist, postdoc fellow at Merck. My research interests primarily focus on cancer biomarker discovery from big transcriptomic data and multi-omics analysis to better understand the ecosystem inside tumor microenvironment.
Biography
I appreciate my interdisciplinary experiences in molecular biology wet lab, bioinformatics and statistics. My expertise includes CRISPR/Cas9 applications, NGS/TGS processing, machine learning and target discovery.
I have a Ph.D. in System, Synthetic and Physical Biology (SSPB) from Rice University.
I graduated from Southern University of Science and Technology(SUSTech) with a B,Sc. in Biology.
Transferrable Skills Highlights
Experience with application of AI/ML in large data analyses, integration, mining and analyses to generate new testable hypotheses.
Previous experience in analyzing DNA and RNA data, interpretation of large DNA and/or RNA sequencing datasets with with clinical covariates
Proficiency in statistical programming languages.
Knowledge of cancer genetics, cancer biology and immunology.
Capable of integrating information generated from multiple sources to strengthen research hypotheses.
Experience with high-performance Linux cluster and cloud computing.
Key Projects
Integration of bulk and single cell RNA-seq data to characterize reproducible subtypes of fibroblasts across tumor types.
Pathway dysfunction in mesenchymal-endothelial interactions promoted LUAD cancer progression.
The Need for Transfer Learning in CRISPR-Cas Off-Target Scoring.
Publications
Pan, Y., Tian, R., Lee, C., Bao, G., and Gibson, G. (2020). Fine-mapping within eQTL Credible Intervals by Expression CROP-seq. Biology Methods and Protocols.
Bao, X. R., Pan, Y., Lee, C. M., Davis, T. H., and Bao, G. (2021). Tools for experimental and com- putational analyses of off-target editing by programmable nucleases. Nature protocols 16, 10–26. Pavan, K., Pan, Y., Vu, H.-A., Cao, M., Baraniuk, R. G., and Bao, G. (2021). The Need for Transfer Learning in CRISPR-Cas Off-Target Scoring. bioRxiv.
Tian, R., Pan, Y., Etheridge, T. H., Deshmukh, H., Gulick, D., Gibson, G., Bao, G., and Lee, C. M. (2020). Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-map Regulatory Intervals. Genes 11, 504.
Park, S. H., Cao, M., Pan, Y., Davis, T., Saxena, L., Deshmukh, H., Fu, Y., Todd, T., Sheehan, V., and Bao, G. (2022). Comprehensive analysis and accurate quantification of unintended large gene modifications induced by CRISPR/Cas9 gene editing. Science Advance - In press.
Sharma, R., Dever, D. P., Lee, C. M., Azizi, A., Pan, Y., Camarena, J., K ̈ohnke, T., Bao, G., Porteus, M. H., and Majeti, R. (2021). The TRACE-Seq method tracks recombination alleles and identifies clonal reconstitution dynamics of gene targeted human hematopoietic stem cells. Nature Communications 12, 1–12.
Pan, Y., and Deem, M. W. (2016). Prediction of influenza B vaccine effectiveness from sequence data. Vaccine 34, 4610–4617