Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME.
Recursive Cluster Elimination based Rank Function (SVM-RCE-R) implemented in KNIME.
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DOI：
10.12688/f1000research.26880.2
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
C Showe L
中科院分区：
文献类型：
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作者：
Yousef M;Bakir-Gungor B;Jabeer A;Goy G;Qureshi R;C Showe L
在我们早期的研究中，我们提出了一种新的特征选择方法，支持向量机递归聚类消除（SVM-RCE），并在Matlab中实现了这种方法。随着时间的推移，人们对这种方法的兴趣越来越大，一些研究人员已经将SVM-RCE纳入他们的研究中，从而产生了大量的科学出版物。这种兴趣的增加鼓励我们重新考虑特征选择，特别是在生物数据集中，如何从考虑这些基因在选择过程中的关系中受益，这导致了我们开发SVM-RCE-R。 SVM-RCE-R通过增加一个新的用户指定的排名功能，进一步增强了SVM-RCE的能力。该排序功能使用户能够在排序功能中规定准确度、灵敏度、特异性、f测量、曲线下面积和精度的权重。该灵活性使用户能够根据特定项目的需要选择更高的灵敏度或更高的特异性。 SVM-RCE-R的有用性进一步得到了mTE工具的开发的支持，该工具使用类似的方法来识别microRNA（miRNA）靶标。我们现在还在Knime中实现了SVM-RCE-R算法，以使其更容易应用。在Knime中使用SVM-RCE-R简单直观，使研究人员能够立即开始分析，而无需咨询信息技术专家。Knime实现工具的输入是一个结构简单的EXCEL文件（或文本或CSV），输出也是一个EXCEL文件。Knime版本还集成了SVM-RCE中没有的新功能。 结果表明，包含的排名功能有显着的影响，SVM-RCE-R的性能。一些集群，实现指定的排名高分也可以有其他指标的高分。
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.