ArticleBMC bioinformatics2013
Combining heterogeneous data sources for accurate functional annotation of proteins.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Expression of the ZIP/SLC39A transporters in β-cells: a systematic review and integration of multiple datasets.BMC genomics · 2017Pooled it
- The ortholog conjecture revisited: the value of orthologs and paralogs in function prediction.Bioinformatics (Oxford, England) · 2020Article
- Combining learning and constraints for genome-wide protein annotation.BMC bioinformatics · 2019Article
- Predicting protein functions by applying predicate logic to biomedical literature.BMC bioinformatics · 2019Article
- NewG3 (Bethesda, Md.) · 2019Article
- Utilization of Electronic Medical Records and Biomedical Literature to Support the Diagnosis of Rare Diseases Using Data Fusion and Collaborative Filtering Approaches.JMIR medical informatics · 2018Article
- Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.Bioinformatics (Oxford, England) · 2018Article
- Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.PLoS computational biology · 2017Article
- Neuro-symbolic representation learning on biological knowledge graphs.Bioinformatics (Oxford, England) · 2017Article
- Large-scale identification of human protein function using topological features of interaction network.Scientific reports · 2016Article
- Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition.Journal of biomedical semantics · 2016Article
- Missing gene identification using functional coherence scores.Scientific reports · 2016Article
- Predicting the functions of a protein from its ability to associate with other molecules.BMC bioinformatics · 2016Article
- Finding New Order in Biological Functions from the Network Structure of Gene Annotations.PLoS computational biology · 2015Article
- The role of ontologies in biological and biomedical research: a functional perspective.Briefings in bioinformatics · 2015Article
- Predicting protein function via downward random walks on a gene ontology.BMC bioinformatics · 2015Article
- PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources.F1000Research · 2015Article
- Integrating multiple networks for protein function prediction.BMC systems biology · 2015Article
- Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct.Journal of biomedical semantics · 2015Article
- A close look at protein function prediction evaluation protocols.GigaScience · 2015Article
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5 authors.
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Abstract
Combining heterogeneous sources of data is essential for accurate prediction of protein function. The task is complicated by the fact that while sequence-based features can be readily compared across species, most other data are species-specific. In this paper, we present a multi-view extension to GOstruct, a structured-output framework for function annotation of proteins. The extended framework can learn from disparate data sources, with each data source provided to the framework in the form of a kernel. Our empirical results demonstrate that the multi-view framework is able to utilize all available information, yielding better performance than sequence-based models trained across species and models trained from collections of data within a given species. This version of GOstruct participated in the recent Critical Assessment of Functional Annotations (CAFA) challenge; since then we have significantly improved the natural language processing component of the method, which now provides performance that is on par with that provided by sequence information. The GOstruct framework is available for download at http://strut.sourceforge.net.
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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.