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Jie Liu

Associate Professor

University of Michigan

Liu Lab

Dr. Liu’s research lab develops computational methods, tools, and resources for understanding the human genome and diseases such as diabetes. Recently, the methodology focuses are knowledge graphs and foundation models. We have developed a knowledge graph GenomicKB to accumulate human-readable knowledge about the human genome. We have extracted genomic knowledge from PubMed and developed another knowledge graph GLKB. We have also developed a genomic foundation model EPCOT which comprehensively predicts multiple genomic modalities.

The lab currently participates in several NIH consortia, including 4DN, IGVF, HIRN, CFDE, and the recent PanKbase program. In particular, Dr. Liu co-leads the Machine Learning Focus Group at IGVF, co-leads the Data/Metadata Working Group at PanKbase, and leads the development of PanKgraph, the knowledge graph within the PanKbase system.

Interests

  • Machine Learning
  • Bioinformatics
  • Computational Genomics
  • Medical Informatics

Education

  • PhD in Computer Science, 2014

    University of Wisconsin - Madison

Recent News

All news»

10/2024 Dr. Liu presented “Building knowledge graphs towards transparent biomedical AI” at NIH/NIDDK AI in Precision Medicine Workshop link

08/2024 Dr. Liu received Endowment for the Basic Sciences Teaching Award 2024. link

08/2024 Yicheng Tao’s CNTools work was published on PLoS CB.

04/2024 Shuze’s work was published on Developmental Cell. link

Projects

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Genomic Knowledge Graph

A graph database for human genome, epigenome, transcriptome, and 4D nucleome.

NucleoMap

A computational tool for identifying nucleosomes in ultra-high resolution contact maps.

ECHO

A deep learning model for characterizing collaborative transcription regulation.

EPCOT

A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.

CAESAR

A deep learning model for connecting high-resolution 3D chromatin organization with epigenomics.

single-cell multi-omics integration

An unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of single cells.

scHi-C embedding

The first computational embedding method for single cells in terms of their chromatin organization.

Multiple Testing Under Dependence

A graphical model based multiple testing procedure which captures dependence among the hypotheses.

Publications

(2024). Liquid biopsy for proliferative diabetic retinopathy: single-cell transcriptomics of human vitreous reveals inflammatory T cell signature. Ophthalmology Science.

DOI

(2024). CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images. PLoS Comput. Biol..

DOI

(2024). Mapping the developmental potential of mouse inner ear organoids at single-cell resolution. iScience.

DOI

(2023). Genetic risk converges on regulatory networks mediating early type 2 diabetes. Nature.

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People

Principal Investigator

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Jie Liu

Associate Professor

Students

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Yuanhao Huang

PhD candidate
DCMB

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Sean Moran

PhD Candidate
DCMB

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Zhenhao Zhang

PhD Candidate
DCMB

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Linghua Jiang

PhD Candidate
DCMB

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Xin Luo

PhD Candidate
DCMB

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Yicheng Tao

PhD Candidate
EECS

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Zheyu Zhang

PhD student
CSE

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Yiqun Wang

PhD Candidate
DCMB

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Lingxiao Guan

PhD Student
EECS

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Xinyu Bao

PhD Student
EECS

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Zhaowei Han

PhD Student
DCMB

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Haohong Shang

ML Engineer
Master student
EECS

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Xuteng Luo

Fullstack Engineer
Master student
EECS

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Meiqi Zhao

UX Design
Master student
School of Information

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Runbo Mao

Master student
Bioinformatics

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Tiancheng Jiao

Fullstack Engineer
Undergraduate
EECS

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Yijun Pan

ML Engineer
Undergraduate
EECS

PhD Alumni

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Shuze Wang

Staff Scientist at MGH

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Fan Feng

Research Fellow at Vanderbilt University

Teaching

Courses

  • Co-instructor, BIOINF 593/EECS 598 Machine Learning in Computational Biology, U-M, 2021 Fall, 2022 Fall, 2023 Fall

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