Ensemble methods of rank-based trees for single sample classification with gene expression profiles
Abstract Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expres...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-024-04940-2 |
| _version_ | 1827326367912427520 |
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| author | Min Lu Ruijie Yin X. Steven Chen |
| author_facet | Min Lu Ruijie Yin X. Steven Chen |
| author_sort | Min Lu |
| collection | DOAJ |
| description | Abstract Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of “relative expression reversals”. Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble . |
| first_indexed | 2024-03-07T14:43:26Z |
| format | Article |
| id | doaj.art-2cf82e1fceca49da9711527fee41003a |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-03-07T14:43:26Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-2cf82e1fceca49da9711527fee41003a2024-03-05T20:07:10ZengBMCJournal of Translational Medicine1479-58762024-02-0122111310.1186/s12967-024-04940-2Ensemble methods of rank-based trees for single sample classification with gene expression profilesMin Lu0Ruijie Yin1X. Steven Chen2Division of Biostatistics, Department of Public Health Sciences, Miller School of Medicine, University of MiamiDivision of Biostatistics, Department of Public Health Sciences, Miller School of Medicine, University of MiamiDivision of Biostatistics, Department of Public Health Sciences, Miller School of Medicine, University of MiamiAbstract Building Single Sample Predictors (SSPs) from gene expression profiles presents challenges, notably due to the lack of calibration across diverse gene expression measurement technologies. However, recent research indicates the viability of classifying phenotypes based on the order of expression of multiple genes. Existing SSP methods often rely on Top Scoring Pairs (TSP), which are platform-independent and easy to interpret through the concept of “relative expression reversals”. Nevertheless, TSP methods face limitations in classifying complex patterns involving comparisons of more than two gene expressions. To overcome these constraints, we introduce a novel approach that extends TSP rules by constructing rank-based trees capable of encompassing extensive gene-gene comparisons. This method is bolstered by incorporating two ensemble strategies, boosting and random forest, to mitigate the risk of overfitting. Our implementation of ensemble rank-based trees employs boosting with LogitBoost cost and random forests, addressing both binary and multi-class classification problems. In a comparative analysis across 12 cancer gene expression datasets, our proposed methods demonstrate superior performance over both the k-TSP classifier and nearest template prediction methods. We have further refined our approach to facilitate variable selection and the generation of clear, precise decision rules from rank-based trees, enhancing interpretability. The cumulative evidence from our research underscores the significant potential of ensemble rank-based trees in advancing disease classification via gene expression data, offering a robust, interpretable, and scalable solution. Our software is available at https://CRAN.R-project.org/package=ranktreeEnsemble .https://doi.org/10.1186/s12967-024-04940-2Single sample predictorDecision treeRank discriminantEnsemble learningBoostingRandom forest |
| spellingShingle | Min Lu Ruijie Yin X. Steven Chen Ensemble methods of rank-based trees for single sample classification with gene expression profiles Journal of Translational Medicine Single sample predictor Decision tree Rank discriminant Ensemble learning Boosting Random forest |
| title | Ensemble methods of rank-based trees for single sample classification with gene expression profiles |
| title_full | Ensemble methods of rank-based trees for single sample classification with gene expression profiles |
| title_fullStr | Ensemble methods of rank-based trees for single sample classification with gene expression profiles |
| title_full_unstemmed | Ensemble methods of rank-based trees for single sample classification with gene expression profiles |
| title_short | Ensemble methods of rank-based trees for single sample classification with gene expression profiles |
| title_sort | ensemble methods of rank based trees for single sample classification with gene expression profiles |
| topic | Single sample predictor Decision tree Rank discriminant Ensemble learning Boosting Random forest |
| url | https://doi.org/10.1186/s12967-024-04940-2 |
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