title: XMRF: an R package to fit Markov Networks to high-throughput genetics data.
abstract: Technological advances in medicine have led to a rapid proliferation of high-throughput "omics" data. Tools to mine this data and discover disrupted disease networks are needed as they hold the key to understanding complicated interactions between genes, mutations and aberrations, and epi-genetic markers.
  
  
  
  We developed an R software package, XMRF, that can be used to fit Markov Networks to various types of high-throughput genomics data. Encoding the models and estimation techniques of the recently proposed exponential family Markov Random Fields (Yang et al., 2012), our software can be used to learn genetic networks from RNA-sequencing data (counts via Poisson graphical models), mutation and copy number variation data (categorical via Ising models), and methylation data (continuous via Gaussian graphical models).
  
  
  
  XMRF is the only tool that allows network structure learning using the native distribution of the data instead of the standard Gaussian. Moreover, the parallelization feature of the implemented algorithms computes the large-scale biological networks efficiently. XMRF is available from CRAN and Github ( https://github.com/zhandong/XMRF ).
authors: Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA.|Department of Human Genetics, Baylor College of Medicine, Houston, Texas, USA.|Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas, USA.
authors: Ying-Wooi
authors: YW
authors: Wan
authors: Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, Texas, USA.|Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas, USA.|Department of Statistics and Electrical & Computer Engineering, Rice University, Houston, Texas, USA.
authors: Genevera I
authors: GI
authors: Allen
authors: Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, Texas, USA.
authors: Yulia
authors: Y
authors: Baker
authors: IBM Thomas J. Waston Research Center, Yorktown Heights, New York, USA.
authors: Eunho
authors: E
authors: Yang
authors: Department of Computer Science, University of Texas, Austin, Texas, USA.
authors: Pradeep
authors: P
authors: Ravikumar
authors: Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA.|Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, Texas, USA.
authors: Matthew
authors: M
authors: Anderson
authors: Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas, USA. zhandonl@bcm.edu.|Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, Texas, USA. zhandonl@bcm.edu.|Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas, USA. zhandonl@bcm.edu.
authors: Zhandong
authors: Z
authors: Liu
mesh_terms: D003196:Computer Graphics; D056915:DNA Copy Number Variations; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing; D008390:Markov Chains; D009154:Mutation; D016012:Poisson Distribution; D017423:Sequence Analysis, RNA; D012984:Software; D013223:Statistics as Topic / Q000379:methods*
keywords: GGM; GLM; Gene network; XMRF