ArticleBMC bioinformatics2010
Predicting gene function using hierarchical multi-label decision tree ensembles.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed.
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation.Bioinformatics (Oxford, England) · 2021Article
- Redescription ofFrontiers in microbiology · 2021Article
- Exploration of Potential miRNA Biomarkers and Prediction for Ovarian Cancer Using Artificial Intelligence.Frontiers in genetics · 2021Article
- Summertime Tintinnid Community in the Surface Waters Across the North Pacific Transition Zone.Frontiers in microbiology · 2021Article
- Frequency spectra characterization of noncoding human genomic sequences.Genes & genomics · 2020Article
- Machine learning: A powerful tool for gene function prediction in plants.Applications in plant sciences · 2020Review
- Distinct Types of Gut Microbiota Dysbiosis in Hospitalized Gastroenterological Patients Are Disease Non-related and Characterized With the Predominance of EitherFrontiers in microbiology · 2020Article
- HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks.BMC medical genomics · 2019Article
- Network inference with ensembles of bi-clustering trees.BMC bioinformatics · 2019Article
- Machine learning for discovering missing or wrong protein function annotations : A comparison using updated benchmark datasets.BMC bioinformatics · 2019Review
- DeepIsoFun: a deep domain adaptation approach to predict isoform functions.Bioinformatics (Oxford, England) · 2019Article
- Gene function prediction based on combining gene ontology hierarchy with multi-instance multi-label learning.RSC advances · 2018Article
- The evolutionary signal in metagenome phyletic profiles predicts many gene functions.Microbiome · 2018Article
- Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.BMC bioinformatics · 2017Article
- Metabolic Pathway Assignment of Plant Genes based on Phylogenetic Profiling-A Feasibility Study.Frontiers in plant science · 2017Article
- Reduction strategies for hierarchical multi-label classification in protein function prediction.BMC bioinformatics · 2016Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundS. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability.
resultsWe study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO). We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use.
conclusionsOur results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.
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