Fuying Dao

Hi, I am Fuying Dao, welcome to my personal page! I am now a postdoctoral fellow of School of Biological Sciences at Nanyang Technological University.

Google Scholar:  Fuying Dao

ORCID: 0000-0001-5285-6044

E-mail: fuying.dao@ntu.edu.sg

Education

Ph.D in Biomedical Engineering (adviser: Dr. Hao Lin), University of Electronic Science and Technology of China, 09/2019 - 06/2023

M.S in Bioinformatics (adviser: Dr. Hao Lin), University of Electronic Science and Technology of China, 09/2016 - 06/2019

B.S in Biotechnology (adviser: Dr. Hao Lin), University of Electronic Science and Technology of China, 09/2012 - 06/2016

Academic Experience

Postdoctoral Scholar (adviser: Dr. Melissa Jane Fullwood), School of Biological Sciences, Nanyang Technological University, 08/2023 - now

Visiting Scholar (adviser: Dr. Melissa Jane Fullwood), School of Biological Sciences, Nanyang Technological University, 10/2021 – 10/2022

Honours and Awards

The Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, Nanyang Technological University, 2023

The China Scholarship Council (CSC) Scholarship, University of Electronic Science and Technology of China, 2021

Research interests

My primary interests are in utilizing high-throughput sequencing data (eg. Hi-C and ChIA-PET) and deep learning algorithms to uncover epigenetic mechanisms associated with disease processes:

*Method development -- How to identify new biological insights from large amounts of available RNA-Seq data? -- Develop deep learning architecture to predict chromatin interactions of large cohorts of cell lines or clinical samples from RNA-Seq data

*The 3D structure of the genome -- Explore differences in the chromatin interactions of different samples to understand how chromatin interactions may be important for the identification of diagnostic and predictive biomarkers for epigenetically driven cancers

Publications

11. Fu-Ying Dao, Meng-Lu Liu, Wei Su, Hao Lv, Zhao-Yue Zhang, Hao Lin*, Li Liu*. (2023) AcrPred: A hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins. International Journal of Biological Macromolecules, 228: 706-714. (2021 IF: 8.025) [Link]

10. Fu-Ying Dao, Hao Lv, Melissa J. Fullwood*, Hao Lin*. (2022) Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information. Research, 2022: ID 9780293. (2021 IF: 11.036) [Link]

9. Fu-Ying Dao, Hao Lv, Zhao-Yue Zhang, Hao Lin*. (2021) BDselect: a package for k-mer selection based on binomial distribution. Current Bioinformatics, 17(3): 238-244(7). (2021 IF: 4.850) [Link]

8. Fu-Ying Dao, Hao Lv, Wei Su, Zi-Jie Sun, Qin-Lai Huang, Hao Lin* (2021) iDHS-Deep: An integrated tool for predicting DNase I hypersensitive sites by deep neural network. Briefings in Bioinformatics, 22(5): bbab047. (2020 IF: 11.622) [Link]

7. Hao Lv&, Fu-Ying Dao&, Hasan Zulfiqar, Wei Su, Hui Ding, Li Liu*, Hao Lin* (2021) A sequence-based deep learning approach to predict CTCF-mediated chromatin loop. Briefings in Bioinformatics, 22(5): bbab031. (2020 IF: 11.622) [Link]

6. Fu-Ying Dao, Hao Lv, Dan Zhang, Zi-Mei Zhang, Li Liu*, Hao Lin*. (2021) DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops. Briefings in Bioinformatics, 22(4):bbaa356. (2020 IF: 11.622) [Link] (ESI)

5. Fu-Ying Dao, Hao Lv, Hasan Zulfiqar, Hui Yang, Wei Su, Hui Gao, Hui Ding, Hao Lin*. (2021) A computational platform to identify origins of replication sites in eukaryotes. Briefings in Bioinformatics, 22(2): 1940–1950. (2020 IF: 11.622) [Link] (ESI)

4. Fu-Ying Dao &, Hao Lv &, Yu-He Yang, Hasan Zulfiqar, Hui Gao, Hao Lin*. (2020) Computational identification of N6-Methyladenosine sites in multiple tissues of mammals. Computational and Structural Biotechnology Journal, 18: 1084–1091. (2019 IF: 6.018) [Link]

3. Fu-Ying Dao, Hao Lv, Fang Wang, Chao-Qin Feng, Hui Ding*, Wei Chen*, Hao Lin*. (2019) Identify origin of replication in Sccharomyces cerevisiae using two-step feature selection technique. Bioinformatics, 35(12):2075-2083. (2018 IF: 4.531) [Link] (ESI,Hot)

2. Fu-Ying Dao, Hao Lv, Fang Wang, Hui Ding*. (2018) Recent advances on the machine learning methods in identifying DNA replication origins in eukaryotic genomics. Frontiers in Genetics, 9: 613. (2017 IF: 4.151) [Link]

1. Fu-Ying Dao, Hui Yang, Zhen-Dong Su, Wuritu Yang, Yun Wu, Ding Hui, Wei Chen*, Hua Tang*, Hao Lin*. (2017) Recent advances in conotoxin classification by using machine learning methods. Molecules, 22: 1057. (2016 IF: 2.861) [Link]

More details can find in my Google Scholar

GitHub Projects

iORI-Epi: a computational approach to predict replication origin sites (ORIs) using the epigenomic marks, DNA motifs, and chromatin loops

iORI-Euk: a computational platform to identify origins of replication sites in eukaryotes

DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops

AcrPred: a hybrid optimization with enumerated machine learning algorithm to predict Anti-CRISPR proteins

BDselect: a package for k-mer selection based on binomial distribution