Shengcheng Dong

Doctoral Student since September 2015
Bioinformatics Candidate

Research areas

  • Non-coding variation
  • Gene Regulation
  • Machine learning

Education

  • B.S.: Tsinghua University

Background

Shengcheng Dong current research focuses on predicting functional non-coding variation related with human diseases by applying machine learning methods.

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.