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Related Experiment Video

Updated: May 14, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Evaluation of analytical methods for connectivity map data.

Jie Cheng¹, Qing Xie, Vinod Kumar

¹Statistical and Platform Technologies, GlaxoSmithKline R&D, UP4335, 1250 S Collegeville Rd, Collegeville, PA 19426, USA. Jie.Cheng@gsk.com

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 21, 2013

Summary

This summary is machine-generated.

This study evaluates drug repositioning methods using gene expression data. The eXtreme cosine (XCos) method shows improved accuracy over existing approaches for predicting drug classifications and finding new drug uses.

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Area of Science:

Pharmacogenomics
Computational Biology
Drug Discovery

Background:

Connectivity map data aids in understanding drug mechanism of action (MOA) and identifying new drug indications.
Systematic evaluations of these methodologies are scarce due to a lack of benchmarking datasets.

Purpose of the Study:

To assess the accuracy of drug repositioning methodologies using gene expression profiles.
To compare the performance of the eXtreme cosine (XCos) method against existing techniques.

Main Methods:

Utilized existing connectivity map data and Anatomical Therapeutic Chemical (ATC) drug classification.
Employed the eXtreme cosine (XCos) method to analyze drug-induced gene expression profile similarity (DIPS).
Compared XCos performance against the Kolmogorov-Smirnov (KS) statistic and the DIPS method using partial area under the curve (AUC).

Main Results:

The XCos method demonstrated superior performance compared to the DIPS method, achieving 17% better results for partial AUC.
Smaller gene signatures (100 probes) outperformed larger ones (500 probes).
DMSO controls from the same batch eliminated the need for mean centering, and prediction accuracy varied across ATC codes.

Conclusions:

The eXtreme cosine (XCos) method offers a more accurate approach for drug repositioning using gene expression data.
Drug transcriptional response is necessary but not sufficient for high prediction accuracy.
Specific drug classes, like corticosteroids, showed higher prediction accuracy due to strong and consistent MOA-related transcriptional responses.