ArticlePLoS computational biology2015
Gene Prioritization by Compressive Data Fusion and Chaining.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Improving drug repositioning accuracy using non-negative matrix tri-factorization.Scientific reports · 2025Article
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
- PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications.Scientific data · 2024Article
- Disease gene prediction with privileged information and heteroscedastic dropout.Bioinformatics (Oxford, England) · 2021Article
- Genome-wide functional association networks: background, data & state-of-the-art resources.Briefings in bioinformatics · 2020Review
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- pBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion.Bioinformatics (Oxford, England) · 2018Article
- Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia.JAMIA open · 2018Article
- Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information.Bioinformatics (Oxford, England) · 2018Article
- Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns.Frontiers in genetics · 2018Article
- A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family.Nature communications · 2017Article
- Article
- Jumping across biomedical contexts using compressive data fusion.Bioinformatics (Oxford, England) · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.