ArticleBMC bioinformatics2008
Gene function prediction using labeled and unlabeled data.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 97 citations in OpenAlex.
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- Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.Current genomics · 2020Article
- Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions.Scientific reports · 2018Article
- Gene function prediction based on combining gene ontology hierarchy with multi-instance multi-label learning.RSC advances · 2018Article
- Machine learning in computational biology to accelerate high-throughput protein expression.Bioinformatics (Oxford, England) · 2017Article
- Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes.BMC bioinformatics · 2017Article
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- Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.Computational and mathematical methods in medicine · 2016Article
- Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach.BMC genomics · 2015Article
- Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines.BioMed research international · 2015Article
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- Negative example selection for protein function prediction: the NoGO database.PLoS computational biology · 2014Article
- Enhanced community structure detection in complex networks with partial background information.Scientific reports · 2013Article
- Predicting gene function using similarity learning.BMC genomics · 2013Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn general, gene function prediction can be formalized as a classification problem based on machine learning technique. Usually, both labeled positive and negative samples are needed to train the classifier. For the problem of gene function prediction, however, the available information is only about positive samples. In other words, we know which genes have the function of interested, while it is generally unclear which genes do not have the function, i.e. the negative samples. If all the genes outside of the target functional family are seen as negative samples, the imbalanced problem will arise because there are only a relatively small number of genes annotated in each family. Furthermore, the classifier may be degraded by the false negatives in the heuristically generated negative samples.
resultsIn this paper, we present a new technique, namely Annotating Genes with Positive Samples (AGPS), for defining negative samples in gene function prediction. With the defined negative samples, it is straightforward to predict the functions of unknown genes. In addition, the AGPS algorithm is able to integrate various kinds of data sources to predict gene functions in a reliable and accurate manner. With the one-class and two-class Support Vector Machines as the core learning algorithm, the AGPS algorithm shows good performances for function prediction on yeast genes.
conclusionWe proposed a new method for defining negative samples in gene function prediction. Experimental results on yeast genes show that AGPS yields good performances on both training and test sets. In addition, the overlapping between prediction results and GO annotations on unknown genes also demonstrates the effectiveness of the proposed method.
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