ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2014
Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A cooperative learning framework for the integration of metabolomic data from multiple cohorts and common phenotype identification.Computational and structural biotechnology journal · 2026Article
- Gene function finding through cross-organism ensemble learning.BioData mining · 2021Article
- GeneWalk identifies relevant gene functions for a biological context using network representation learning.Genome biology · 2021Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Augmenting subnetwork inference with information extracted from the scientific literature.PLoS computational biology · 2019Article
- Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images.Entropy (Basel, Switzerland) · 2019Article
- Network enhancement as a general method to denoise weighted biological networks.Nature communications · 2018Article
- Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia.JAMIA open · 2018Article
- COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- Gene Prioritization by Compressive Data Fusion and Chaining.PLoS computational biology · 2015Article
- GUDM: Automatic Generation of Unified Datasets for Learning and Reasoning in Healthcare.Sensors (Basel, Switzerland) · 2015Article
- Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.PLoS computational biology · 2015Article
- Yeast as a cell factory: current state and perspectives.Microbial cell factories · 2015Review
- Computational algorithms to predict Gene Ontology annotations.BMC bioinformatics · 2015Article
- Integration of molecular network data reconstructs Gene Ontology.Bioinformatics (Oxford, England) · 2014Article
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Authors and funding
2 authors.
Funding
Abstract
The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression profiles, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker's yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps.
Indexed as
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24297565PMC3902649What OpenQuestion holds
Registered trials
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