title: Gene Prioritization by Compressive Data Fusion and Chaining.
abstract: 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.
authors.affiliation: Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
authors.forename: Marinka
authors.initials: M
authors.lastname: Žitnik
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.
authors.forename: Edward A
authors.initials: EA
authors.lastname: Nam
authors.affiliation: Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, United States of America.
authors.forename: Christopher
authors.initials: C
authors.lastname: Dinh
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America; Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, United States of America.
authors.forename: Adam
authors.initials: A
authors.lastname: Kuspa
authors.affiliation: Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.
authors.forename: Gad
authors.initials: G
authors.lastname: Shaulsky
authors.affiliation: Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America.
authors.forename: Blaž
authors.initials: B
authors.lastname: Zupan
mesh_terms: D049109:Cell Proliferation / Q000502:physiology; D044962:Data Compression / Q000379:methods*; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D004023:Dictyostelium / Q000378:metabolism* / Q000382:microbiology*; D006090:Gram-Negative Bacteria / Q000502:physiology*; D015800:Protozoan Proteins / Q000235:genetics / Q000378:metabolism*
chemical_list: D015800:Protozoan Proteins