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Gene prioritization by compressive data fusion and chaining
Zitnik, Marinka (Avtor), Nam, Edward A (Avtor), Dinh, Christopher (Avtor), Kuspa, Adam (Avtor), Shaulsky, Gad (Avtor), Zupan, Blaz (Avtor)

URLURL - Predstavitvena datoteka, za dostop obiščite http://eprints.fri.uni-lj.si/3209/ Novo okno

Izvleček
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.

Jezik:Neznan jezik
Ključne besede:data mining, data fusion, gene prioritization, bacterial resistance, dictyostelium
Vrsta gradiva:Delo ni kategorizirano (r6)
Organizacija:FRI - Fakulteta za računalništvo in informatiko
Leto izida:2015
Št. strani:e1004552
Številčenje:11
DOI:10.1371/journal.pcbi.1004552 Povezava se odpre v novem oknu
Število ogledov:419
Število prenosov:156
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
 
Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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