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Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications.

Hao He¹, Hongrui Duo², Youjin Hao²

¹College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China; State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, PR China.

Computers in Biology and Medicine

|February 22, 2023

Summary

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This summary is machine-generated.

Drug repurposing offers an effective alternative to traditional drug development. This review highlights transcriptome data

Area of Science:

Computational biology
Pharmacology
Genomics

Background:

De novo drug development is complex, costly, and time-consuming.
There is an urgent need for efficient therapeutic development methods.
Drug repurposing presents a promising alternative for disease treatment.

Purpose of the Study:

To review the role of transcriptome data in computational drug repurposing.
To summarize existing databases, tools, and strategies for transcriptome data analysis.
To propose a guideline for effectively mining transcriptome data to identify new drug indications.

Main Methods:

Literature review of computational drug repurposing strategies.
Analysis of the utility of transcriptome data in identifying new drug uses.

Keywords:

Computational drug repurposing Drug combination Drug discovery Gene expression profiling RNA-seq

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Development of a framework correlating gene expression data types with mining strategies.

Main Results:

Transcriptome data plays a crucial role in computational drug repurposing.
Various databases, tools, and strategies exist for analyzing gene expression data.
A practical guideline is proposed to match data types with strategies for effective mining.

Conclusions:

Transcriptome data is invaluable for computational drug repurposing.
Efficiently mining this data is key to discovering novel therapeutic applications for existing drugs.
The proposed guideline will aid researchers in leveraging transcriptome data for drug discovery.