Cited by 85Gene expression and cancer classificationBayesian Methods and Mixture ModelsBioinformatics and Genomic Networks

Bayesian joint analysis of heterogeneous genomics data

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Ray, Priyadip, Zheng, Lingling, Lucas, Joseph, Carin, Lawrence·2014·Bioinformatics

Abstract

SUMMARY: A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is demonstrated by jointly analyzing gene expression/copy number variations and gene expression/methylation data for ovarian cancer patients, showing that the proposed model can potentially uncover key drivers related to cancer. AVAILABILITY AND IMPLEMENTATION: The source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/. CONTACT: catherine.ll.zheng@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Authors
Ray, P., Zheng, L., Lucas, J., & Carin, L.
Affiliations
  • Indian Institute of Technology Kharagpur (IN)
  • 1 G.S.Sanyal School of Telecommunications, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India, 2Computational Biology & Bioinformatics, Duke University, Durham, NC 27705, USA, 3Quintiles, Durham, NC 27703, USA and 4Electrical & Computer Engineering Department, Duke University, Durham, NC 27705, USA
Venue
Bioinformatics
Published
2014-01-30
Volume
30
Issue
10
Pages
1370-1376
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W2130186565
Cited by
84
References
56
Citations by year
2026: 2 · 2025: 6 · 2024: 3 · 2023: 5 · 2022: 8 · 2021: 8 · 2020: 9 · 2019: 9 · 2018: 9 · 2017: 13 · 2016: 4 · 2015: 4 · 2014: 3 · 2013: 1
FWCI
3.28
Type
journal article
Language
en
Open access
Locations
  • Bioinformatics (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Biomedical Advanced Research and Development Authority
  • Office of Naval Research
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
ovarian cancer, cancer drivers
MeSH terms
Bayes Theorem, Female, Gene Expression Regulation, Humans, Ovarian Neoplasms, Software, DNA Methylation, Genomics, DNA Copy Number Variations

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