Adaptive hypergraph regularized logistic regression model for bioinformatic selection and classification
Abstract The classification of cancer using established biological knowledge has become increasingly prevalent, primarily due to the improved accuracy and enhanced biological interpretability this method offers for classification outcomes. Despite these advances, current cancer classification methods encounter challenges in maintaining the intricate structure of gene networks and leveraging the statistical information embedded within gene data. In this paper, we introduce an adaptive hypergraph regularized logistic regression model that capitalizes on established biological knowledge and statistical information within gene data. Specifically, our model integrates a hypergraph into the objective function, an innovation that preserves the complex gene network structure more effectively. Additionally, we implement adaptive penalties in the penalty term, which facilitates the targeted selection of disease-related genes based on gene weights. To further refine our model, we incorporate constraints on gene pairs with high statistical correlations within the penalty term, thereby minimizing the inclusion of redundant genes. We adopt the block coordinate descent algorithm to address the nonconvexity of our model. Through comparative experimentation with established methodologies on real datasets, our proposed model demonstrates marked improvement in classification accuracy and adept selection of genes pertinent to specific diseases..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:54 |
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| Enthalten in: |
Applied intelligence - 54(2024), 3 vom: Feb., Seite 2349-2360 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jin, Yong [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| BKL: | |
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| Themen: |
Cancer classification |
| Anmerkungen: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| doi: |
10.1007/s10489-024-05304-5 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract The classification of cancer using established biological knowledge has become increasingly prevalent, primarily due to the improved accuracy and enhanced biological interpretability this method offers for classification outcomes. Despite these advances, current cancer classification methods encounter challenges in maintaining the intricate structure of gene networks and leveraging the statistical information embedded within gene data. In this paper, we introduce an adaptive hypergraph regularized logistic regression model that capitalizes on established biological knowledge and statistical information within gene data. Specifically, our model integrates a hypergraph into the objective function, an innovation that preserves the complex gene network structure more effectively. Additionally, we implement adaptive penalties in the penalty term, which facilitates the targeted selection of disease-related genes based on gene weights. To further refine our model, we incorporate constraints on gene pairs with high statistical correlations within the penalty term, thereby minimizing the inclusion of redundant genes. We adopt the block coordinate descent algorithm to address the nonconvexity of our model. Through comparative experimentation with established methodologies on real datasets, our proposed model demonstrates marked improvement in classification accuracy and adept selection of genes pertinent to specific diseases. | ||
| 650 | 4 | |a Logistic regression |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Cancer classification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feature selection |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Hou, Huaibin |0 (orcid)0009-0008-2346-0268 |4 aut | |
| 700 | 1 | |a Qin, Mian |4 aut | |
| 700 | 1 | |a Yang, Wei |4 aut | |
| 700 | 1 | |a Zhang, Zhen |4 aut | |
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