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


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

Honors and Awards

  • Rackham Graduate Student Research Grant (candidate)


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 papers

  1. Shigaki D, Adato O, Adhikar AN, Dong S, Hawkins-Hooker A, Inoue F, Juven-Gershon T, Kenlay H, Martin B, Patra A, Penar DP, Schubach M, Xiong C, Yan Z, Boyle AP, Kreimer A, Kulakovskiy IV, Reid J, Unger R, Yosef N, Shendure J, Ahituv N, Kircher M, and Beer MA. 2019. Integration of Multiple Epigenomic Marks Improves Prediction of Variant Impact in Saturation Mutagenesis Reporter Assay. Human Mutation. 40: 1280-1291. DOI: 10.1002/humu.23797.

  2. Nishizaki SS, Ng N, Dong S, Porter RS, Morterud C, Williams C, Asman C, Switzenberg JA, and Boyle AP. 2019. Predicting the effects of SNPs on transcription factor binding affinity. Bioinformatics. 50: 2434. DOI: 10.1093/bioinformatics/btz612.

  3. Dong S and Boyle AP. 2019. Predicting functional variants in enhancer and promoter elements using RegulomeDB. Human Mutation. 40: 1292-1298. DOI: 10.1002/humu.23791.

  4. Dong S and Boyle AP. 2021. Preprint: Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome. DOI: .