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Updated: Jul 3, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Computational protocol to identify shared transcriptional risks and mutually beneficial compounds between diseases.

Hua Gao¹, Mao Zhang², Richard A Baylis³

¹Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Cardiovascular Institute, Stanford, CA 94305, USA.

|February 14, 2024

Summary

This study introduces a computational protocol to find shared disease mechanisms and screen drugs for repurposing. It uses omics data and electronic health records to identify beneficial compounds for novel treatments.

Keywords:

Bioinformatics Cancer Health Sciences RNAseq

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

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Published on: June 27, 2020

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Area of Science:

Genomics and Bioinformatics
Translational Medicine
Computational Biology

Background:

Growing omics and biobank data enable genome-wide analysis of shared disease pathways.
Drug repurposing offers a faster route to novel therapeutics by leveraging existing compounds.

Purpose of the Study:

To present a computational protocol for identifying shared transcriptional processes between diseases.
To screen compounds for potential therapeutic benefits and drug repurposing opportunities.
To describe a pharmacovigilance study for validating compound effects using real-world electronic health records.

Main Methods:

Implementation of a Snakemake workflow for computational analysis.
Utilizing omics data and biobank resources for genome-wide insights.
Employing electronic health records for pharmacovigilance and validation.

Main Results:

Identification of shared transcriptional processes across diseases.
Screening of compounds for potential mutual therapeutic benefit.
Validation framework for drug effects using real-world data.

Conclusions:

The protocol facilitates the discovery of shared disease mechanisms and novel therapeutic strategies.
It enables efficient screening of compounds for drug repurposing.
Integration with pharmacovigilance studies enhances the validation of identified compounds.