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Predicting transcriptional changes induced by molecules with MiTCP.

Kaiyuan Yang¹, Jiabei Cheng¹, Shenghao Cao¹

¹Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.

Briefings in Bioinformatics

|January 23, 2025

Summary

Researchers developed a deep learning method, Molecule-induced Transcriptional Change Predictor (MiTCP), to predict cellular transcriptional changes caused by molecules. This approach aids drug discovery by accurately forecasting gene expression alterations, outperforming existing methods.

Keywords:

changes of transcriptional profiles graph neural networks small molecules

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Last Updated: May 31, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Published on: March 7, 2018

Efficient Chromatin Immunoprecipitation using Limiting Amounts of Biomass

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Published on: May 1, 2013

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

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Published on: June 15, 2016

Area of Science:

Computational Biology
Genomics
Drug Discovery

Background:

Understanding cellular responses to small molecules is vital for drug discovery.
Experimental methods for profiling transcriptional changes are time-consuming and costly.

Purpose of the Study:

To develop a deep learning model, MiTCP, for predicting molecule-induced transcriptional changes.
To accurately forecast changes in transcriptional profiles (CTPs) of 978 landmark genes.

Main Methods:

Utilized graph neural networks to model molecular structure and gene co-expression.
Trained the MiTCP model on the L1000 dataset.
Integrated molecular structure and gene relationships for CTP prediction.

Main Results:

Achieved an average Pearson correlation coefficient (PCC) of 0.482 on the test set.
Demonstrated high accuracy in predicting top differentially expressed genes (PCC of 0.801).
Outperformed existing methods in CTP prediction.

Conclusions:

MiTCP shows potential in accelerating drug development by predicting drug-induced gene expression changes.
Enrichment analysis of predicted CTPs for cancer drugs revealed disease-relevant pathways.
The method offers a faster and potentially more cost-effective alternative to experimental profiling.