Shengcheng Dong, Ph.D.

PhD student from September 2015 to March 2021
Postdoc from April 2021 to February 2022
Bioinformatics Ph.D.
Current: Postdoctoral Scholar, Stanford University

Research areas

  • Non-coding variation
  • Gene Regulation
  • Machine learning

Education

  • B.S.: Tsinghua University
  • Ph.D.: University of Michigan

Honors and Awards

  • Rackham Graduate Student Research Grant (candidate)

Background

Shengcheng Dong current research focuses on predicting functional non-coding variation related with human diseases by applying machine learning methods. She finished her Ph.D. in the Boyle lab in March 2021.

Boyle Lab publications (4)

Integration of Multiple Epigenomic Marks Improves Prediction of Variant Impact in Saturation Mutagenesis Reporter Assay

Human Mutation 2019 40(9):1280-1291
DOI PDF PubMed BibTeX

Predicting the effects of SNPs on transcription factor binding affinity

Bioinformatics 2019 50:2434
DOI PDF PubMed Preprint BibTeX

Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome

2021
DOI BibTeX

Predicting functional variants in enhancer and promoter elements using RegulomeDB

Human Mutation 2019 40(9):1292-1298
DOI PDF PubMed BibTeX