Machine learning methods for metabolic pathway prediction
<p>Abstract</p> <p>Background</p> <p>A 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...
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| Format: | Article |
| Language: | English |
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BMC
2010-01-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/11/15 |
| _version_ | 1811264823969710080 |
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| author | Karp Peter D Popescu Liviu Dale Joseph M |
| author_facet | Karp Peter D Popescu Liviu Dale Joseph M |
| author_sort | Karp Peter D |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>A 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.</p> <p>Results</p> <p>To 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.</p> <p>Conclusions</p> <p>ML 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.</p> |
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| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T20:11:23Z |
| publishDate | 2010-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-4ed588c9092d4faead02aef9db59364e2022-12-22T03:18:14ZengBMCBMC Bioinformatics1471-21052010-01-011111510.1186/1471-2105-11-15Machine learning methods for metabolic pathway predictionKarp Peter DPopescu LiviuDale Joseph M<p>Abstract</p> <p>Background</p> <p>A 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.</p> <p>Results</p> <p>To 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.</p> <p>Conclusions</p> <p>ML 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.</p>http://www.biomedcentral.com/1471-2105/11/15 |
| spellingShingle | Karp Peter D Popescu Liviu Dale Joseph M Machine learning methods for metabolic pathway prediction BMC Bioinformatics |
| title | Machine learning methods for metabolic pathway prediction |
| title_full | Machine learning methods for metabolic pathway prediction |
| title_fullStr | Machine learning methods for metabolic pathway prediction |
| title_full_unstemmed | Machine learning methods for metabolic pathway prediction |
| title_short | Machine learning methods for metabolic pathway prediction |
| title_sort | machine learning methods for metabolic pathway prediction |
| url | http://www.biomedcentral.com/1471-2105/11/15 |
| work_keys_str_mv | AT karppeterd machinelearningmethodsformetabolicpathwayprediction AT popesculiviu machinelearningmethodsformetabolicpathwayprediction AT dalejosephm machinelearningmethodsformetabolicpathwayprediction |