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首页> 外文会议>2015 International Conference on Advances in Computer Engineering and Applications >Feature based classification of nuclear receptors and their subfamilies using fuzzy K-nearest neighbor
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Feature based classification of nuclear receptors and their subfamilies using fuzzy K-nearest neighbor

机译：基于特征的模糊K近邻算法对核受体及其亚家族进行分类

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摘要

The efficient classification of nuclear receptors and their subfamilies plays an important role in the detection of various diseases such as diabetes, cancer, and inflammatory diseases and their related drug design and discovery. As of now, few methods have been reported in literature for the same but the performance and efficacy of these methods are not up to the desired level. To address the issue of efficient classification of nuclear receptor and their subfamilies, here in this paper we propose to use a fuzzy k-nearest neighbor classifier with minimum redundancy maximum relevance for the classification of nuclear receptor and their eight subfamilies. The minimum redundancy maximum relevance algorithm is used to select the optimal feature subset and observed that highest accuracy and Matthew's correlation coefficient is obtained with 400 features among 753 features through fuzzy kNN classifier. The performance of fuzzy kNN classifier depends on two parameter number of nearest neighbor (k) and fuzzy coefficient (m) and it is observed that the highest accuracy and MCC is obtained at k=7 and m= 1.25. The overall accuracies of 10 fold cross validation with optimal number of features, k and m are 98.09% and 97.85% and the MCC values of 0.97 and 0.90 for the prediction of nuclear receptor families and subfamilies respectively. From the obtained results and analysis it is observed that the performance of the proposed approach for the classification of nuclear receptor and their eight subfamilies is very competitive with some other standard methods available in literature.
机译：核受体及其亚家族的有效分类在各种疾病（例如糖尿病，癌症和炎性疾病）及其相关药物设计和发现的检测中起着重要作用。到目前为止，文献中几乎没有报道过相同的方法，但是这些方法的性能和功效尚未达到期望的水平。为了解决核受体及其亚家族的有效分类问题，在本文中，我们建议使用具有最小冗余最大相关性的模糊k最近邻分类器对核受体及其八个亚家族进行分类。采用最小冗余最大相关算法选择最优特征子集，通过模糊kNN分类器，利用753个特征中的400个特征获得了最高的精度和马修相关系数。模糊kNN分类器的性能取决于最近邻居（k）和模糊系数（m）的两个参数，并且观察到在k = 7和m = 1.25时可以获得最高的精度和MCC。具有最佳特征数量k和m的10倍交叉验证的总体准确性分别为98.09％和97.85％，MCC值为0.97和0.90，分别用于预测核受体家族和亚家族。从获得的结果和分析可以看出，所提出的用于核受体及其八个亚家族分类的方法的性能与文献中提供的其他一些标准方法非常有竞争力。

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