High dimensional model representation of log-likelihood ratio: binary classification with expression data
Abstract Background Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene inte...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-020-3486-x |
| _version_ | 1819104362223370240 |
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| author | Ali Foroughi pour Maciej Pietrzak Lori A Dalton Grzegorz A. Rempała |
| author_facet | Ali Foroughi pour Maciej Pietrzak Lori A Dalton Grzegorz A. Rempała |
| author_sort | Ali Foroughi pour |
| collection | DOAJ |
| description | Abstract Background Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene interactions, and (b) the need for highly interpretable glass-box models. We use the theory of high dimensional model representation (HDMR) to build interpretable low dimensional approximations of the log-likelihood ratio accounting for the effects of each individual gene as well as gene-gene interactions. We propose two algorithms approximating the second order HDMR expansion, and a hypothesis test based on the HDMR formulation to identify significantly dysregulated pairwise interactions. The theory is seen as flexible and requiring only a mild set of assumptions. Results We apply our approach to gene expression data from both synthetic and real (breast and lung cancer) datasets comparing it also against several popular state-of-the-art methods. The analyses suggest the proposed algorithms can be used to obtain interpretable prediction rules with high prediction accuracies and to successfully extract significantly dysregulated gene-gene interactions from the data. They also compare favorably against their competitors across multiple synthetic data scenarios. Conclusion The proposed HDMR-based approach appears to produce a reliable classifier that additionally allows one to describe how individual genes or gene-gene interactions affect classification decisions. Both real and synthetic data analyses suggest that our methods can be used to identify gene networks with dysregulated pairwise interactions, and are therefore appropriate for differential networks analysis. |
| first_indexed | 2024-12-22T02:05:09Z |
| format | Article |
| id | doaj.art-1ced15e011974644812d310744bd54f6 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T02:05:09Z |
| publishDate | 2020-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-1ced15e011974644812d310744bd54f62022-12-21T18:42:33ZengBMCBMC Bioinformatics1471-21052020-04-0121112710.1186/s12859-020-3486-xHigh dimensional model representation of log-likelihood ratio: binary classification with expression dataAli Foroughi pour0Maciej Pietrzak1Lori A Dalton2Grzegorz A. Rempała3Department of Electrical and Computer Engineering, The Ohio State UniversityDepartment of Biomedical Informatics, The Ohio State UniversityDepartment of Electrical and Computer Engineering, The Ohio State UniversityDepartment of Mathematics, The Ohio State UniversityAbstract Background Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene interactions, and (b) the need for highly interpretable glass-box models. We use the theory of high dimensional model representation (HDMR) to build interpretable low dimensional approximations of the log-likelihood ratio accounting for the effects of each individual gene as well as gene-gene interactions. We propose two algorithms approximating the second order HDMR expansion, and a hypothesis test based on the HDMR formulation to identify significantly dysregulated pairwise interactions. The theory is seen as flexible and requiring only a mild set of assumptions. Results We apply our approach to gene expression data from both synthetic and real (breast and lung cancer) datasets comparing it also against several popular state-of-the-art methods. The analyses suggest the proposed algorithms can be used to obtain interpretable prediction rules with high prediction accuracies and to successfully extract significantly dysregulated gene-gene interactions from the data. They also compare favorably against their competitors across multiple synthetic data scenarios. Conclusion The proposed HDMR-based approach appears to produce a reliable classifier that additionally allows one to describe how individual genes or gene-gene interactions affect classification decisions. Both real and synthetic data analyses suggest that our methods can be used to identify gene networks with dysregulated pairwise interactions, and are therefore appropriate for differential networks analysis.http://link.springer.com/article/10.1186/s12859-020-3486-xHigh dimensional model representationClassificationDisease predictionLog-likelihood ratioExpression analysis |
| spellingShingle | Ali Foroughi pour Maciej Pietrzak Lori A Dalton Grzegorz A. Rempała High dimensional model representation of log-likelihood ratio: binary classification with expression data BMC Bioinformatics High dimensional model representation Classification Disease prediction Log-likelihood ratio Expression analysis |
| title | High dimensional model representation of log-likelihood ratio: binary classification with expression data |
| title_full | High dimensional model representation of log-likelihood ratio: binary classification with expression data |
| title_fullStr | High dimensional model representation of log-likelihood ratio: binary classification with expression data |
| title_full_unstemmed | High dimensional model representation of log-likelihood ratio: binary classification with expression data |
| title_short | High dimensional model representation of log-likelihood ratio: binary classification with expression data |
| title_sort | high dimensional model representation of log likelihood ratio binary classification with expression data |
| topic | High dimensional model representation Classification Disease prediction Log-likelihood ratio Expression analysis |
| url | http://link.springer.com/article/10.1186/s12859-020-3486-x |
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