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Updated: Jul 9, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO).

Enakshi Saha¹, Viola Fanfani¹, Panagiotis Mandros¹

¹Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.

Biorxiv : the Preprint Server for Biology

|November 28, 2023

Summary

This summary is machine-generated.

BONOBO infers individual gene co-expression networks, revealing biological differences between samples. This Bayesian approach captures heterogeneity missed by traditional methods, offering new insights into gene regulation.

Keywords:

Bayesian inference Co-expression Gene regulatory network individual-specific network posterior distribution

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Last Updated: Jul 9, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Published on: December 10, 2012

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Gene regulatory networks (GRNs) model molecular interactions crucial for biological processes.
Co-expression network inference is key for GRN analysis, but existing methods often overlook individual variations.
Population-level networks fail to capture sample-specific regulatory heterogeneity.

Conclusions:

BONOBO offers a scalable and effective approach to uncover sample-specific gene regulatory differences.
The method enhances understanding of biological heterogeneity and its impact on gene regulation.
BONOBO provides valuable insights into individual variations driving biological processes.