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Related Experiment Video

Updated: Mar 31, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

Gene Prioritization by Compressive Data Fusion and Chaining.

Marinka Žitnik¹, Edward A Nam², Christopher Dinh³

¹Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.

Plos Computational Biology

|October 15, 2015

Summary

This summary is machine-generated.

Collage, a novel data fusion method, effectively prioritizes genes by integrating diverse datasets. This approach successfully identified key bacterial response genes in Dictyostelium, advancing biological knowledge discovery.

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Area of Science:

Systems Biology
Bioinformatics
Genomics

Background:

Current data integration methods for predictive modeling are often limited to data directly related to the target object, such as genes.
There is a need for advanced approaches to fuse heterogeneous data with varying association levels for biological discovery.

Purpose of the Study:

To introduce Collage, a novel data fusion approach designed for gene prioritization.
To demonstrate Collage's capability in integrating diverse datasets, including those with indirect relevance to the prediction task.
To validate Collage's effectiveness in identifying biologically relevant genes using a novel model system.

Main Methods:

Collage utilizes collective matrix factorization to compress heterogeneous data.
The method employs chaining to connect different object types within a data compendium.
Gene prioritization is achieved by assessing similarity to predefined seed genes.

Main Results:

Collage successfully prioritized genes related to bacterial response in Dictyostelium, a model for prokaryote-eukaryote interactions.
Using 14 datasets with varying relevance, Collage identified 8 candidate genes.
All 8 candidate genes were experimentally validated as essential for Dictyostelium's response to Gram-negative bacteria.

Conclusions:

Collage is an effective method for inferring biological knowledge through the integration of heterogeneous and indirectly related datasets.
The approach offers a powerful tool for gene prioritization and discovery in complex biological systems.