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.
PhD in Computer Science, 2014
University of Wisconsin - Madison
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!
A graph database for human genome, epigenome, transcriptome, and 4D nucleome.
A computational tool for identifying nucleosomes in ultra-high resolution contact maps.
A deep learning model for characterizing collaborative transcription regulation.
A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.
A deep learning model for connecting high-resolution 3D chromatin organization with epigenomics.
An unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of single cells.
The first computational embedding method for single cells in terms of their chromatin organization.
A graphical model based multiple testing procedure which captures dependence among the hypotheses.