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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

Automated Protocol for Large-Scale Modeling of Gene Expression Data.

Michelle Lynn Hall¹, David Calkins², Woody Sherman¹

¹Schrödinger, Inc. , 222 Third Street, Cambridge, Massachusetts 02143, United States.

Journal of Chemical Information and Modeling

|November 1, 2016

Summary

This summary is machine-generated.

Automated machine learning models predict differential gene expression based on compound structure. This approach achieves over 70% accuracy, enabling virtual screening for desired gene expression profiles.

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Area of Science:

Computational biology
Pharmacogenomics
Machine learning

Background:

Phenotypic and genotypic screening projects are increasing.
Computational methods are crucial for analyzing screening data and making predictions.
Predicting gene expression changes due to compound structures is a key challenge.

Purpose of the Study:

To demonstrate automated machine learning workflows for predicting differential gene expression from compound structures.
To establish predictive models using A673 cell data as a proof of principle.
To enable virtual screening and lead optimization for multitarget gene expression profiles.

Main Methods:

Utilized automated machine learning workflows.
Trained models to predict differential gene expression as a function of compound structure.
Validated models using A673 cell data.

Main Results:

Achieved predictive models with average accuracy greater than 70%.
Models demonstrated predictive power across a diverse set of approximately 1000 gene expression profiles.
Successfully predicted differential gene expression based on compound structure.

Conclusions:

Automated machine learning offers a powerful approach for predicting gene expression.
This method moves beyond traditional target-based in silico design.
The developed models facilitate virtual screening and lead optimization for desired multitarget gene expression profiles.