ArticlePLoS computational biology2007
Global discriminative learning for higher-accuracy computational gene prediction.
Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- An overview of computational methods for gene prediction in eukaryotes: strengths, limitations, and future directions.Bioinformatics advances · 2025Review
- The nuclear and mitochondrial genomes of Frieseomelitta varia - a highly eusocial stingless bee (Meliponini) with a permanently sterile worker caste.BMC genomics · 2020Article
- Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation.Sensors (Basel, Switzerland) · 2020Article
- A Study of Domain Adaptation Classifiers Derived From Logistic Regression for the Task of Splice Site Prediction.IEEE transactions on nanobioscience · 2016Article
- A large-scale proteogenomics study of apicomplexan pathogens-Toxoplasma gondii and Neospora caninum.Proteomics · 2015Article
- Integration of RNA-seq and proteomics data with genomics for improved genome annotation in Apicomplexan parasites.Proteomics · 2015Article
- Article
- Effective automated feature construction and selection for classification of biological sequences.PloS one · 2014Article
- Next-generation annotation of prokaryotic genomes with EuGene-P: application to Sinorhizobium meliloti 2011.DNA research : an international journal for rapid publication of reports on genes and genomes · 2013Article
- A beginner's guide to eukaryotic genome annotation.Nature reviews. Genetics · 2012Review
- Classifier assessment and feature selection for recognizing short coding sequences of human genes.Journal of computational biology : a journal of computational molecular cell biology · 2012Article
- Article
- Reassessing domain architecture evolution of metazoan proteins: major impact of gene prediction errors.Genes · 2011Article
- Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data.Computational systems bioinformatics. Computational Systems Bioinformatics Conference · 2010Article
- Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis.BMC bioinformatics · 2010Article
- Unifying generative and discriminative learning principles.BMC bioinformatics · 2010Article
- mGene: accurate SVM-based gene finding with an application to nematode genomes.Genome research · 2009Article
- Identification of natural killer cell receptor clusters in the platypus genome reveals an expansion of C-type lectin genes.Immunogenetics · 2009Article
- mGene.web: a web service for accurate computational gene finding.Nucleic acids research · 2009Article
- The 2008 update of the Aspergillus nidulans genome annotation: a community effort.Fungal genetics and biology : FG & B · 2009Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM) in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.
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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.