Carnegie Mellon University ———
Mellon College of Science  ›  Computational Cancer Research  ›  Faculty  ›  Jian Ma

Jian Ma

Associate Professor, Computational Biology

Address:
GHC 7705, School of Computer Science
Carnegie Mellon University
4400 Fifth Avenue
Pittsburgh, PA 15213

Email

Jian Ma
We are developing new methodologies to determine important alterations in tumor cells. (1) Identifying important genomic alterations, especially non-coding mutations, in cancer. (2) 3D genome organization features and their roles in cancer. (3) The roles of structural variants and copy number alterations in cancer genomes. (4) Machine learning methods to characterize genomic patterns in cancer and their connections to other data modalities such as imaging data.

Highlighted Publications

Rajaraman A and Ma J. Towards recovering allele-specific cancer genome graphs. RECOMB, 2017.

Li Y, Zhou S, Schwartz DC, and Ma J. Allele-specific quantification of structural variations in cancer genomes. Cell Systems, 3(1):21-34, 2016. RECOMB, 2016.

Hou JP†, Emad A†, Puleo GJ, Ma J*, and Milenkovic O*. A new correlation clustering method for cancer mutation analysis. Bioinformatics, 32(24):3717-28, 2016.

Hou JP and Ma J. DawnRank: Discovering personalized driver genes in cancer. Genome Medicine, 6:56, 2014.

Liu Y, Gu Q, Hou JP, Han J, and Ma J. A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression. BMC Bioinformatics, 15:37, 2014.

Li Y, Chien J, Smith DI, and Ma J. FusionHunter: identifying fusion transcripts in cancer using paired-end RNA-seq. Bioinformatics, 27(12):1708-10, 2011.

GOOGLE SCHOLAR

Software

Discrimantive Bag-of-Cells (DBC) - machine learning tool for predicting artibtrary cellular images

C3  

LDGM - estimate the differential gene regulatory network between two biological conditions such as tumor subtypes

Weaver - returns allele-specific copy numbers of regions and phased breakpoints of structural variants

DawnRank - discovering personalized driver mutations in cancer

NCIS - a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression