ArticleBMC bioinformatics2010
Machine learning methods for metabolic pathway prediction.
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 73 papers.
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73 citing papers in PubMed.
- Using supervised machine-learning approaches to understand abiotic stress tolerance and design resilient crops.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Learning motif features and topological structure of molecules for metabolic pathway prediction.Journal of cheminformatics · 2025Article
- Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers.Biomolecules & biomedicine · 2025Review
- A deep learning architecture for metabolic pathway prediction.Bioinformatics (Oxford, England) · 2024Article
- Predicting pathways for old and new metabolites through clustering.Journal of theoretical biology · 2024Article
- Multi-label classification with XGBoost for metabolic pathway prediction.BMC bioinformatics · 2024Article
- Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.Frontiers in immunology · 2024Article
- Machine learning for metabolic pathway optimization: A review.Computational and structural biotechnology journal · 2023Review
- Predicting metabolic fluxes from omics data via machine learning: Moving from knowledge-driven towards data-driven approaches.Computational and structural biotechnology journal · 2023Article
- Metabolomics and modelling approaches for systems metabolic engineering.Metabolic engineering communications · 2022Review
- Exploring synergies between plant metabolic modelling and machine learning.Computational and structural biotechnology journal · 2022Review
- Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing.Biodesign research · 2022Review
- Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook.Briefings in bioinformatics · 2021Article
- Leveraging heterogeneous network embedding for metabolic pathway prediction.Bioinformatics (Oxford, England) · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology.Briefings in bioinformatics · 2021Review
- New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review.Frontiers in bioengineering and biotechnology · 2021Review
- Metabolic pathway inference using multi-label classification with rich pathway features.PLoS computational biology · 2020Article
- Computational Methods for Predicting Functions at the mRNA Isoform Level.International journal of molecular sciences · 2020Review
- Machine learning: A powerful tool for gene function prediction in plants.Applications in plant sciences · 2020Review
13 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
backgroundA key challenge in systems biology is the reconstruction of an organism's metabolic network from its genome sequence. One strategy for addressing this problem is to predict which metabolic pathways, from a reference database of known pathways, are present in the organism, based on the annotated genome of the organism.
resultsTo quantitatively validate methods for pathway prediction, we developed a large "gold standard" dataset of 5,610 pathway instances known to be present or absent in curated metabolic pathway databases for six organisms. We defined a collection of 123 pathway features, whose information content we evaluated with respect to the gold standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway prediction using the gold standard dataset. We found that ML-based prediction methods can match the performance of the PathoLogic algorithm. PathoLogic achieved an accuracy of 91% and an F-measure of 0.786. The ML-based prediction methods achieved accuracy as high as 91.2% and F-measure as high as 0.787. The ML-based methods output a probability for each predicted pathway, whereas PathoLogic does not, which provides more information to the user and facilitates filtering of predicted pathways.
conclusionsML methods for pathway prediction perform as well as existing methods, and have qualitative advantages in terms of extensibility, tunability, and explainability. More advanced prediction methods and/or more sophisticated input features may improve the performance of ML methods. However, pathway prediction performance appears to be limited largely by the ability to correctly match enzymes to the reactions they catalyze based on genome annotations.
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