ArticleBioinformatics (Oxford, England)2020
Bayesian structural equation modeling in multiple omics data with application to circadian genes.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- A parametric survival model with bayesian structural equation based on multi-omics integration.BMC bioinformatics · 2025Article
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- A clustering approach to integrative analyses of multiomic cancer data.Journal of applied statistics · 2025Article
- Application of Bayesian genomic prediction methods to genome-wide association analyses.Genetics, selection, evolution : GSE · 2022Review
- Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2021Article
- Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data.Scientific reports · 2020Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationIt is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions, which might be dormant in a single-source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes' omics profile, such as copy number changes and RNA-sequence data along with their survival response. We develop a Bayesian structural equation modeling coupled with linear regressions and log normal accelerated failure-time regression to integrate the information between these two platforms to predict the survival of the subjects. We place conjugate priors on the regression parameters and derive the Gibbs sampler using the conditional distributions of them.
resultsOur extensive simulation study shows that the integrative model provides a better fit to the data than its closest competitor. The analyses of glioblastoma cancer data and the breast cancer data from TCGA, the largest genomics and transcriptomics database, support our findings. AVAILABILITY AND IMPLEMENTATION: The developed method is wrapped in R package available at https://github.com/MAITYA02/semmcmc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.