ArticleGenome biology2008
A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.
Article in Genome biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers.
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139 citing papers in PubMed, 257 citations in OpenAlex.
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- Structure-based protein function prediction using graph convolutional networks.Nature communications · 2021Article
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- A holistic view of mouse enhancer architectures reveals analogous pleiotropic effects and correlation with human disease.BMC genomics · 2020Article
- CoCoCoNet: conserved and comparative co-expression across a diverse set of species.Nucleic acids research · 2020Article
- Supervised learning is an accurate method for network-based gene classification.Bioinformatics (Oxford, England) · 2020Article
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- Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.PLoS biology · 2020Article
- A Literature Review of Gene Function Prediction by Modeling Gene Ontology.Frontiers in genetics · 2020Review
- Novel comparison of evaluation metrics for gene ontology classifiers reveals drastic performance differences.PLoS computational biology · 2019Article
- UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction.BMC bioinformatics · 2019Article
- Towards region-specific propagation of protein functions.Bioinformatics (Oxford, England) · 2019Article
- Prediction of seed gene function in progressive diabetic neuropathy by a network-based inference method.Experimental and therapeutic medicine · 2019Article
- Optimal pathways involved in the treatment of sevoflurane or propofol for patients undergoing coronary artery bypass graft surgery.Experimental and therapeutic medicine · 2019Article
- Identification of prognostic biomarkers in colorectal cancer using a long non-coding RNA-mediated competitive endogenous RNA network.Oncology letters · 2019Article
- deepNF: deep network fusion for protein function prediction.Bioinformatics (Oxford, England) · 2018Article
79 more citing papers are in PubMed but not listed here.
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Authors and funding
40 authors at 16 institutions in 6 countries.
Funding
Abstract
backgroundSeveral years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.
resultsIn this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.
conclusionWe performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.
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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.