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Indiana University School of Medicine
Indiana University School of Medicine
Biomedical Data Research Lab develops new computational methods to mine and integrate biomedical omics data and develops new statistical learning methods for semi-supervised or unsupervised pattern detection from high dimensional data.

Biomedical Data Research Lab

Biomedical Data Research Lab is a computational systems biology lab led by Chi Zhang, PhD, and Sha Cao, PhD, from Indiana University School of Medicine. The lab develops new computational methods to mine and integrate biomedical omics data and develops new statistical learning methods for semi-supervised or unsupervised pattern detection from high dimensional data. The current research projects include method development for (1) biological explainable model of cellular and tissue level changes from scRNA-seq and bulk tissue data, (2) cell type specific transcriptional regulation from single cell multi-omics data, and (3) matrix, tensor and high order tensor local low rank representation.

Research Updates

Stay up-to-date on research at IU School of Medicine on our blog.

Current Research Funding

NIGHMS 1R01GM131399-01 Construction of cell type specific gene co-regulation signatures based on single cell transcriptomics data

The major goal of this project is to construct a computational infrastructure to derive cell type specific gene co-regulation signatures that can form reference map of certain cell types by integrating single cell RNA-Seq data with epi-genomics data.

NCI U54CA196519-01 (CoI) Developmental and HyperActive Ras Tumor

Recent Publications

  1. Changlin Wan, Wennan Chang, Yu Zhang, Fenil Shah, Xiaoyu Lu, Yong Zang, Anru Zhang, Sha Cao, Melissa L Fishel, Qin Ma, Chi Zhang*, LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data, Nucleic Acids Research, Volume 47, Issue 18, 10 October 2019, Page e111, https://doi.org/10.1093/nar/gkz655
  2. Juan Xie, Anjun Ma, Yu Zhang, Bingqiang Liu, Sha Cao, Cankun Wang, Jennifer Xu, Chi Zhang*, Qin Ma*, QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data, Bioinformatics, btz692, https://doi.org/10.1093/bioinformatics/btz692
  3. Yu Zhang, Changlin Wan, Pengcheng Wang, Wennan Chang, Yan Huo, Jian Chen, Qin Ma, Sha Cao, Chi Zhang*. M3S: A comprehensive model selection for multi-modal single-cell RNA sequencing data. BMC Bioinformatics. 2019 Dec 20;20(Suppl 24):672. doi: 10.1186/s12859-019-3243-1.
  4. Changlin Wan, Wennan Chang, Tong Zhao, Mengya Li, Sha Cao, Chi Zhang*. MEBF: a fast and efficient Boolean matrix factorization method. 2020 AAAI 2020 conference. arXiv:1909.03991
  5. Andrea M Gross, Pamela L Wolters, Eva Dombi, Andrea Baldwin, Patricia Whitcomb, Michael J Fisher, Brian Weiss, AeRang Kim, Miriam Bornhorst, Amish C Shah, Staci Martin, Marie C Roderick, Dominique C Pichard, Amanda Carbonell, Scott M Paul, Janet Therrien, Oxana Kapustina, Kara Heisey, D Wade Clapp, Chi Zhang, Cody J Peer, William D Figg, Malcolm Smith, John Glod, Jaishri O Blakeley, Seth M Steinberg, David J Venzon, L Austin Doyle, Brigitte C Widemann. Selumetinib in Children with Inoperable Plexiform Neurofibromas. 2020. New England Journal of Medicine 382 (15), 1430-1442

Bibliography

Research Team

Wennan Chang, PhD student
Changlin Wan, PhD student
Xiaoyu Lu, PhD student
Pengtao Dang, PhD student
Norah Alghamdi, PhD student
Alex White, PhD student
Haiqi Zhu, Master student
Kaman So, Master Student