dictyNews
Electronic Edition
Volume 41, number 20
September 18, 2015

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Abstracts
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Gene prioritization by compressive data fusion and chaining

Marinka Žitnik, Edward A. Nam, Christopher Dinh, Adam Kuspa, 
Gad Shaulsky & Blaž Zupan

University of Ljubljana, Ljubljana, Slovenia
Baylor College of Medicine, Houston, TX, USA

PLoS Comput. Biol. Accepted for publication

Data integration procedures combine heterogeneous data sets 
into predictive models, but they are limited to data explicitly 
related to the target object type, such as genes. Collage is a 
new data fusion approach to gene prioritization. It considers 
data sets of various association levels with the prediction 
task, utilizes collective matrix factorization to compress the 
data, and chaining to relate different object types contained 
in a data compendium. Collage prioritizes genes based on their 
similarity to several seed genes. We tested Collage by 
prioritizing bacterial response genes in Dictyostelium as a 
novel model system for prokaryote-eukaryote interactions. 
Using 4 seed genes and 14 data sets, only one of which was 
directly related to the bacterial response, Collage proposed 
8 candidate genes that were readily validated as necessary 
for the response of Dictyostelium to Gram-negative bacteria. 
These findings establish Collage as a method for inferring 
biological knowledge from the integration of heterogeneous 
and coarsely related data sets.

Submitted by Gad Shaulsky [gadi@bcm.edu]   
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[End dictyNews, volume 41, number 20]