Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME [version 2; peer review: 1 approved, 2 approved with reservations]
In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2021-01-01
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| Series: | F1000Research |
| Online Access: | https://f1000research.com/articles/9-1255/v2 |
| _version_ | 1818700531609108480 |
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| author | Malik Yousef Burcu Bakir-Gungor Amhar Jabeer Gokhan Goy Rehman Qureshi Louise C. Showe |
| author_facet | Malik Yousef Burcu Bakir-Gungor Amhar Jabeer Gokhan Goy Rehman Qureshi Louise C. Showe |
| author_sort | Malik Yousef |
| collection | DOAJ |
| description | In our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. SVM-RCE-R, further enhances the capabilities of SVM-RCE by the addition of a novel user specified ranking function. This ranking function enables the user to stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify microRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics. |
| first_indexed | 2024-12-17T15:06:26Z |
| format | Article |
| id | doaj.art-15bdb8c81a31468eb7d6a0fc31ee842b |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-12-17T15:06:26Z |
| publishDate | 2021-01-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-15bdb8c81a31468eb7d6a0fc31ee842b2022-12-21T21:43:47ZengF1000 Research LtdF1000Research2046-14022021-01-01910.12688/f1000research.26880.231433Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME [version 2; peer review: 1 approved, 2 approved with reservations]Malik Yousef0Burcu Bakir-Gungor1Amhar Jabeer2Gokhan Goy3Rehman Qureshi4Louise C. Showe5Zefat Academic College, Zefat, IsraelDepartment of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, TurkeyThe Wistar Institute, Philadelphia, PA, USAThe Wistar Institute, Philadelphia, PA, USAIn our earlier study, we proposed a novel feature selection approach, Recursive Cluster Elimination with Support Vector Machines (SVM-RCE) and implemented this approach in Matlab. Interest in this approach has grown over time and several researchers have incorporated SVM-RCE into their studies, resulting in a substantial number of scientific publications. This increased interest encouraged us to reconsider how feature selection, particularly in biological datasets, can benefit from considering the relationships of those genes in the selection process, this led to our development of SVM-RCE-R. SVM-RCE-R, further enhances the capabilities of SVM-RCE by the addition of a novel user specified ranking function. This ranking function enables the user to stipulate the weights of the accuracy, sensitivity, specificity, f-measure, area under the curve and the precision in the ranking function This flexibility allows the user to select for greater sensitivity or greater specificity as needed for a specific project. The usefulness of SVM-RCE-R is further supported by development of the maTE tool which uses a similar approach to identify microRNA (miRNA) targets. We have also now implemented the SVM-RCE-R algorithm in Knime in order to make it easier to applyThe use of SVM-RCE-R in Knime is simple and intuitive and allows researchers to immediately begin their analysis without having to consult an information technology specialist. The input for the Knime implemented tool is an EXCEL file (or text or CSV) with a simple structure and the output is also an EXCEL file. The Knime version also incorporates new features not available in SVM-RCE. The results show that the inclusion of the ranking function has a significant impact on the performance of SVM-RCE-R. Some of the clusters that achieve high scores for a specified ranking can also have high scores in other metrics.https://f1000research.com/articles/9-1255/v2 |
| spellingShingle | Malik Yousef Burcu Bakir-Gungor Amhar Jabeer Gokhan Goy Rehman Qureshi Louise C. Showe Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME [version 2; peer review: 1 approved, 2 approved with reservations] F1000Research |
| title | Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_full | Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_fullStr | Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_full_unstemmed | Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_short | Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_sort | recursive cluster elimination based rank function svm rce r implemented in knime version 2 peer review 1 approved 2 approved with reservations |
| url | https://f1000research.com/articles/9-1255/v2 |
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