Jie Liu

Assistant Professor

University of Michigan

Liu Lab

Dr. Liu’s research lab develops computational approaches for understanding the functions of the human genome and the genetic basis of human diseases. We recently have developed a knowledge graph GenomicKB to accumulate human-readable knowledge about the human genome. We have also developed a computational framework EPCOT which comprehensively predicts multiple genomic modalities, and allows computers to accumulate knowledge about the human genome. In addition, we have imputed high-resolution chromatin contact maps for GTEx donors with CAESAR.

Liu Lab currently has openings for graduate students and postdocs. Please reach out to Dr. Jie Liu via email if you are interested.

Interests

Machine Learning
Bioinformatics
Computational Genomics
Medical Informatics

Education

PhD in Computer Science, 2014

University of Wisconsin - Madison

Recent News

All news»

05/2023 Our paper titled “A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome” was accepted for publication on Nucleic Acids Research. Congratulations to Zhenhao and the rest of the team!

04/2023 Congratulations to Shuze who successfully defended her PhD thesis and will join Massachusetts General Hospital as a staff scientist!

11/2022 Our paper titled “GenomicKB: a knowledge graph for the human genome” was accepted for publication on Nucleic Acids Research. Congratulations to Fan and the rest of the team!

06/2022 Our paper titled “NucleoMap: a computational tool for identifying nucleosomes in ultra-high resolution contact maps” was accepted for publication on PLOS Computational Biology. Congratulations to Yuanhao and Bingjiang!

05/2022 Our paper titled “Characterizing collaborative transcription regulation with a graph-based deep learning approach” was accepted for publication on PLOS Computational Biology. Congratulations to Zhenhao and Fan!

Projects

Publications

Z Zhang, F Fang, Y Qiu, J Liu (2023). A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome. Nucleic Acids Res.

PDF Code Project Web Portal

F Feng, F Tang, Y Gao, D Zhu, T Li, S Yang, Y Yao, Y Huang, J Liu (2022). GenomicKB: a knowledge graph for the human genome. Nucleic Acids Res.

PDF Project DOI Web Portal