bioRxivSystems BiologyPreprint
A Bayesian network approach for modeling mixed features in TCGA ovarian cancer data
Abstract
We propose an integrative framework to select important genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a logistic Bayesian network model based on The Cancer Genome Atlas data. The constructed Bayesian network has identified four gene clusters of distinct cellular functions, 13 driver genes, as well as some new biological pathways which may shed new light into the molecular mechanisms of ovarian cancer.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: Bayesian inference
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Driver Pattern Identification Over The Gene Co-Expression Of Drug Response In Ovarian Cancer By Integrating High Throughput Genomics Data
Xinguo Lu, Jibo Lu, … Keqin Li·Scientific ReportsPeer-reviewed·Nov 17, 2017
- New analysis framework incorporating mixed mutual information and scalable Bayesian networks for multimodal high dimensional genomic and epigenomic cancer data
Xichun Wang, Sergio Branciamore, … Andréi S. Rodin·Frontiers in GeneticsPeer-reviewed·Jun 18, 2020
- Oncogenetic Network Estimation with Disjunctive Bayesian Networks
Phillip B. Nicol, Kevin R. Coombes, … Amir Asiaee·Computational and Systems OncologyPeer-reviewed·Jun 1, 2021
- A Mixture Copula Bayesian Network Model for Multimodal Genomic Data
Qingyang Zhang, Xuan Shi·Cancer InformaticsPeer-reviewed·Jan 1, 2017
- A Bayesian Network-Based Framework for Causal Cancer Drug Target Discovery Integrating Patient and Cell Line Data
Seok Hwan Yoon, Ye Rin Park, Hyun Uk Kim·bioRxiv·Jul 13, 2026
- Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data
Yanming Li, Mihaela E. Sardiu, … Murshalina Akhter·medRxiv·Dec 21, 2023