MORF: Multi-view oblique random forest for hepatotoxicity prediction
Summary: Hepatotoxicity prediction is significant in drug development, so the experts expect to get effective and reliable references. Based on the consideration that the hepatotoxicity data involve multiple types of features, this paper proposes a multi-view oblique random forest (MORF) for hepatot...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225000148 |
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| author | Binsheng Sui Qingzhuo He Bowei Yan Kunhong Liu Yong Xu Song He Xiaochen Bo |
| author_facet | Binsheng Sui Qingzhuo He Bowei Yan Kunhong Liu Yong Xu Song He Xiaochen Bo |
| author_sort | Binsheng Sui |
| collection | DOAJ |
| description | Summary: Hepatotoxicity prediction is significant in drug development, so the experts expect to get effective and reliable references. Based on the consideration that the hepatotoxicity data involve multiple types of features, this paper proposes a multi-view oblique random forest (MORF) for hepatotoxicity prediction by considering each type of feature as an independent view. The Householder transformation is employed to get the inclined cut hyperplane in each view. Two versions of the multi-view oblique decision tree (ODT) algorithms were designed by generating optimal nodes based on selecting proper hyperplanes from different views, named ODT-N and ODT-R. These two types of ODT algorithms serve as the base learners to construct two MORF. Experiments conducted on the hepatotoxicity data provide performance comparisons among different algorithms, and the results confirm that our algorithms can fully utilize the information in different views. |
| format | Article |
| id | doaj-art-c16161d5cc7c44fdb72ba86ff32dd970 |
| institution | Kabale University |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-c16161d5cc7c44fdb72ba86ff32dd9702025-01-22T05:43:07ZengElsevieriScience2589-00422025-02-01282111755MORF: Multi-view oblique random forest for hepatotoxicity predictionBinsheng Sui0Qingzhuo He1Bowei Yan2Kunhong Liu3Yong Xu4Song He5Xiaochen Bo6Department of Digital Media, Xiamen University, Xiamen 361005, ChinaWee School of Communication and Information, Nanyang Technological University, Singapore, SingaporeInstitutes of Biomedical Sciences, Fudan University, Shanghai 200433, ChinaDepartment of Digital Media, Xiamen University, Xiamen 361005, China; Xiamen Key Laboratory of Intelligent Storage and Computing, School of Informatics, Xiamen University, Xiamen 361005, China; Corresponding authorXiamen Key Laboratory of Intelligent Fishery, Xiamen Ocean Vocational College, Xiamen 361100, ChinaInstitute of Health Service and Transfusion Medicine, Beijing 100850, China; Corresponding authorInstitute of Health Service and Transfusion Medicine, Beijing 100850, China; Corresponding authorSummary: Hepatotoxicity prediction is significant in drug development, so the experts expect to get effective and reliable references. Based on the consideration that the hepatotoxicity data involve multiple types of features, this paper proposes a multi-view oblique random forest (MORF) for hepatotoxicity prediction by considering each type of feature as an independent view. The Householder transformation is employed to get the inclined cut hyperplane in each view. Two versions of the multi-view oblique decision tree (ODT) algorithms were designed by generating optimal nodes based on selecting proper hyperplanes from different views, named ODT-N and ODT-R. These two types of ODT algorithms serve as the base learners to construct two MORF. Experiments conducted on the hepatotoxicity data provide performance comparisons among different algorithms, and the results confirm that our algorithms can fully utilize the information in different views.http://www.sciencedirect.com/science/article/pii/S2589004225000148BioinformaticsCancer |
| spellingShingle | Binsheng Sui Qingzhuo He Bowei Yan Kunhong Liu Yong Xu Song He Xiaochen Bo MORF: Multi-view oblique random forest for hepatotoxicity prediction iScience Bioinformatics Cancer |
| title | MORF: Multi-view oblique random forest for hepatotoxicity prediction |
| title_full | MORF: Multi-view oblique random forest for hepatotoxicity prediction |
| title_fullStr | MORF: Multi-view oblique random forest for hepatotoxicity prediction |
| title_full_unstemmed | MORF: Multi-view oblique random forest for hepatotoxicity prediction |
| title_short | MORF: Multi-view oblique random forest for hepatotoxicity prediction |
| title_sort | morf multi view oblique random forest for hepatotoxicity prediction |
| topic | Bioinformatics Cancer |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225000148 |
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