ArticleThe annals of applied statistics
BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA.
Article in The annals of applied statistics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Bayesian Transfer Learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- SpaceX: gene co-expression network estimation for spatial transcriptomics.Bioinformatics (Oxford, England) · 2022Article
- Classification and prediction for multi-cancer data with ultrahigh-dimensional gene expressions.PloS one · 2022Article
- Bayesian Structure Learning in Multi-layered Genomic Networks.Journal of the American Statistical Association · 2021Article
- Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.Biometrics · 2020Article
- NExUS: Bayesian simultaneous network estimation across unequal sample sizes.Bioinformatics (Oxford, England) · 2020Article
- Article
- Fast Bayesian inference in large Gaussian graphical models.Biometrics · 2019Article
- Integration of Survival and Binary Data for Variable Selection and Prediction: A Bayesian Approach.Journal of the Royal Statistical Society. Series C, Applied statistics · 2019Article
- Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness.Biostatistics (Oxford, England) · 2019Article
- Spectral Clustering via sparse graph structure learning with application to Proteomic Signaling Networks in Cancer.Computational statistics & data analysis · 2019Article
- Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.Statistical modelling · 2017Article
- Integrative bayesian network analysis of genomic data.Cancer informatics · 2014Article
- BAYESIAN SPARSE GRAPHICAL MODELS FOR CLASSIFICATION WITH APPLICATION TO PROTEIN EXPRESSION DATA.The annals of applied statisticsArticle
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
Reverse-phase protein array (RPPA) analysis is a powerful, relatively new platform that allows for high-throughput, quantitative analysis of protein networks. One of the challenges that currently limit the potential of this technology is the lack of methods that allow for accurate data modeling and identification of related networks and samples. Such models may improve the accuracy of biological sample classification based on patterns of protein network activation and provide insight into the distinct biological relationships underlying different types of cancer. Motivated by RPPA data, we propose a Bayesian sparse graphical modeling approach that uses selection priors on the conditional relationships in the presence of class information. The novelty of our Bayesian model lies in the ability to draw information from the network data as well as from the associated categorical outcome in a unified hierarchical model for classification. In addition, our method allows for intuitive integration of a priori network information directly in the model and allows for posterior inference on the network topologies both within and between classes. Applying our methodology to an RPPA data set generated from panels of human breast cancer and ovarian cancer cell lines, we demonstrate that the model is able to distinguish the different cancer cell types more accurately than several existing models and to identify differential regulation of components of a critical signaling network (the PI3K-AKT pathway) between these two types of cancer. This approach represents a powerful new tool that can be used to improve our understanding of protein networks in cancer.
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Registered trials
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.