Education

University of Michigan – Ann Arbor, Depart. of Computational Medicine and Bioinformatics (DCMB)
Ph.D. Bioinformatics, Sep. 2018 -
Advisor: Dr. Jie Liu

Peking University, College of Chemistry and Molecular Engineering (CCME)
BSc. Chemical Biology, Sep. 2014 - Jul. 2018
Awards for Outstanding Graduates of Peking University and CCME

Peking University, National School of Development (NSD)
Dual Bachelor of Economics, Sep. 2015 - Jul. 2018

Publication

Accepted or published: (*co-first author)
  • Zhang, Z., Feng, F., Qiu, Y., & Liu., J. (2023). A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome. Nucleic Acids Research, gkad436
  • Feng, F., Tang, F., Gao, Y., Zhu, D., ... & Liu, J. GenomicKB: a knowledge graph for human genomics. Nucleic Acids Research, 51.D1 (2023): D950-D956.
  • Zhang, Z., Feng, F., & Liu, J. (2022). Characterizing collaborative transcription regulation with a graph-based deep learning approach. PLoS Computational Biology, 18(6), e1010162.
  • Feng, F., Yao, Y., Wang, X. Q. D., Zhang, X., & Liu, J. (2022). Connecting high-resolution 3D chromatin organization with epigenomics. Nature Communications, 13(1), 1-10.
  • Li, X., Feng, F., Pu, H., Leung, W. Y., & Liu, J. (2021). scHiCTools: A computational toolbox for analyzing single-cell Hi-C data. PLoS Computational Biology, 17(5), e1008978.
  • Himadewi, P.*, Wang, X. Q. D.*, Feng, F.*, Gore, H., Liu, Y., Yu, L., ... & Zhang, X. (2021). 3’HS1 CTCF binding site in human β-globin locus regulates fetal hemoglobin expression. Elife, 10, e70557.
  • Zhang, X., Wang, X. Q. D., Gore, H., Himadewi, P., Feng, F., & Liu, J. (2020). 3D Genomics of Acute Meyloid Leukemia Reveals the Imbalance between DNA Methylation Canyon Interactions and Leukemic Specific Enhancer Network Interactions. Blood, 136, 45.
  • Feng, F., Lai, L., & Pei, J. (2018). Computational chemical synthesis analysis and pathway design. Frontiers in Chemistry, 6, 199.
Submitted, under review, or preprint:
  • Zhang, Z., Feng, F., Qiu, Y., & Liu., J. A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome (under review by Nucleic Acids Research)
  • Tao., Y., Feng, F., Luo, X., & Liu., J. CNTools: A computational toolbox for cellular neighborhood analysis from cell images (under review by Nature Communications)
  • Feng, F., Moran, S. P., & Liu, J. Quagga: a stripe caller for chromatin contact maps (submitted)
  • Wang, X. Q. D., Gore, H., Himadewi, P., Feng, F., Yang, L., ... & Zhang, X. (2020). Three-dimensional regulation of HOXA cluster genes by a cis-element in hematopoietic stem cell and leukemia. bioRxiv.

Invited Talks

  • “GenomicKB: a knowledge graph for human genome”, Intellectual Systems of Molecular Biology (ISMB), Lyon, France, July 2023
  • “A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome”, Great Lakes Bioinformatics (GLBIO) conference, Montreal, Canada, May 2023
  • “An overview of Hi-C downstream analysis”, Chromatin 3D Folding Workshop, UT Health, Houston, USA, March 2023
  • “GenomicKB: a knowledge graph for human genome”, Tools and Technology Seminar, University of Michigan, Ann Arbor, USA, January 2023
  • “Connecting high-resolution 3D chromatin organization with epigenomics”, Intellectual Systems of Molecular Biology (ISMB), Madison, USA, July 2022
  • “An accurate and interpretable model for predicting high-resolution 3D chromatin organization”, 4DN Consortium Joint Analysis Working Group, September 2019

Teaching

  • Graduate Student Instructor (GSI), BIOINF 593/EECS 598 Machine Learning in Computational Biology, University of Michigan, 2022 Fall

Invited Peer Review Experience

  • Reviewer, PLoS Computational Biology, since 2022
  • Reviewer, Conference on Neural Information Processing Systems (NeurIPS) AI4Science Workshop, since 2021
  • Reviewer, International Conference on Machine Learning (ICML) AI4Science Workshop, since 2022

Research Interest

  • Using statistical and machine learning models to jointly analyze 3D chromatin structural information with genomic features in order to understand the mechanisms of gene regulation in diseases
  • Develop open web tools and machine learning platforms based on knowledge graphs to jointly analyze human genomic/epigenomic/transcriptome/4D genome data
  • Develop deep learning interpretation tools for bioinformatics models
  • Develop tools to analyze chromatin 3D structural patterns in Hi-C contact maps