Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, GD 518055, China.
Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Bioinformatics. 2020 Dec 8;36(19):4968-4969. doi: 10.1093/bioinformatics/btaa621.
Nowadays, it is feasible to collect massive features for quantitative representation and precision medicine, and thus, automatic ranking to figure out the most informative and discriminative ones becomes increasingly important. To address this issue, 42 feature ranking (FR) methods are integrated to form a MATLAB toolbox (matFR). The methods apply mutual information, statistical analysis, structure clustering and other principles to estimate the relative importance of features in specific measure spaces. Specifically, these methods are summarized, and an example shows how to apply a FR method to sort mammographic breast lesion features. The toolbox is easy to use and flexible to integrate additional methods. Importantly, it provides a tool to compare, investigate and interpret the features selected for various applications.
The toolbox is freely available at http://github.com/NicoYuCN/matFR. A tutorial and an example with a dataset are provided.
如今，为了进行定量表示和精准医学，可以收集大量的特征，因此，自动排序以找出最具信息量和区分度的特征变得越来越重要。针对这个问题，我们整合了 42 种特征排序（FR）方法，形成了一个 MATLAB 工具箱（matFR）。这些方法应用互信息、统计分析、结构聚类等原理来估计特征在特定度量空间中的相对重要性。具体来说，我们对这些方法进行了总结，并通过一个示例展示了如何应用 FR 方法对乳腺病变特征进行排序。该工具箱易于使用且灵活，可以集成其他方法。重要的是，它为比较、研究和解释各种应用中选择的特征提供了一种工具。
该工具箱可在 http://github.com/NicoYuCN/matFR 上免费获取。我们提供了一个教程和一个带有数据集的示例。
