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Updated: Jul 1, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

TransNeT-CGP: A cluster-based comorbid gene prioritization by integrating transcriptomics and network-topological

K R Saranya¹, E R Vimina¹, F R Pinto²

¹Department of Computer Science & IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India.

Computational Biology and Chemistry

|March 10, 2024

Summary

This summary is machine-generated.

This study introduces a novel computational method to identify key genes driving comorbid diseases by analyzing protein-protein interaction networks. The approach effectively prioritizes genes involved in overlapping disease mechanisms, aiding in the development of targeted gene therapies.

Keywords:

Gene Prioritization Comorbidity Clustering Protein-Protein Interaction Network

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Computational Biology and Bioinformatics
Systems Biology
Genetics and Genomics

Background:

Gene disruptions in one disease can impact pathways of other diseases, leading to comorbidity.
Prioritizing key genes regulating common biological mechanisms is crucial for effective gene therapy in overlapping diseases.

Purpose of the Study:

To propose a clustering-based computational approach for prioritizing comorbid genes within overlapping disease modules.
To analyze Protein-Protein Interaction (PPI) networks to identify key regulatory genes in comorbid conditions.

Main Methods:

Extracted disease-pair subnetworks from the interactome.
Assigned edge weights using gene expression correlation and betweenness centrality.
Applied weighted graph clustering, ranking dominant nodes by clustering coefficients and neighborhood connectivity.

Main Results:

The proposed method identified more associated pathways and disease-specific protein complexes compared to existing methods (SAPDSB, S2B) in case studies (ALS-SMA, OC-IDBC).
Top-ranked genes significantly disrupted network connectivity upon removal, indicating their critical role.
Functional and pathway enrichment analyses confirmed the mechanistic relevance of the identified key genes.

Conclusions:

The proposed clustering-based computational method is effective for identifying key genes in comorbidity.
This approach offers valuable insights into the intricate molecular relationships driving comorbid diseases.
The findings can guide the development of more precise gene therapies for complex, overlapping conditions.