ArticleBioinformatics (Oxford, England)2011
Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error.Psychological methods · 2023Trial
- Graph-guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information.Statistics in biosciences · 2024Article
- NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.Briefings in bioinformatics · 2024Article
- Feature selection and classification over the network with missing node observations.Statistics in medicine · 2022Article
- Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression.BMC bioinformatics · 2021Article
- Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks.PLoS computational biology · 2020Article
- Bayesian Non-linear Support Vector Machine for High-Dimensional Data with Incorporation of Graph Information on Features.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2019Article
- Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology.JCO precision oncology · 2019Review
- A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images.Biostatistics (Oxford, England) · 2019Article
- Article
- A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD.Statistics in biosciences · 2018Article
- A Bayesian Gene-Based Genome-Wide Association Study Analysis of Osteosarcoma Trio Data Using a Hierarchically Structured Prior.Cancer informatics · 2018Article
- Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields.Bioinformatics (Oxford, England) · 2017Article
- Joint Bayesian variable and graph selection for regression models with network-structured predictors.Statistics in medicine · 2016Article
- A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data.Bioinformatics (Oxford, England) · 2016Article
- Gene network inference by fusing data from diverse distributions.Bioinformatics (Oxford, England) · 2015Article
- Bayesian Inference of Multiple Gaussian Graphical Models.Journal of the American Statistical Association · 2015Article
- Principles and methods of integrative genomic analyses in cancer.Nature reviews. Cancer · 2014Review
- The common ground of genomics and systems biology.BMC systems biology · 2014Review
- Joint network and node selection for pathway-based genomic data analysis.Bioinformatics (Oxford, England) · 2013Article
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2 authors.
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Abstract
motivationDiscriminant analysis is an effective tool for the classification of experimental units into groups. Here, we consider the typical problem of classifying subjects according to phenotypes via gene expression data and propose a method that incorporates variable selection into the inferential procedure, for the identification of the important biomarkers. To achieve this goal, we build upon a conjugate normal discriminant model, both linear and quadratic, and include a stochastic search variable selection procedure via an MCMC algorithm. Furthermore, we incorporate into the model prior information on the relationships among the genes as described by a gene-gene network. We use a Markov random field (MRF) prior to map the network connections among genes. Our prior model assumes that neighboring genes in the network are more likely to have a joint effect on the relevant biological processes.
resultsWe use simulated data to assess performances of our method. In particular, we compare the MRF prior to a situation where independent Bernoulli priors are chosen for the individual predictors. We also illustrate the method on benchmark datasets for gene expression. Our simulation studies show that employing the MRF prior improves on selection accuracy. In real data applications, in addition to identifying markers and improving prediction accuracy, we show how the integration of existing biological knowledge into the prior model results in an increased ability to identify genes with strong discriminatory power and also aids the interpretation of the results.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.