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An entropy-based improved k-top scoring pairs (TSP) method for classifying human cancers

机译：基于熵的改进k-top得分对（TSP）方法对人类癌症进行分类

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Classification and prediction of different cancers based on gene-expression profiles are important for cancer diagnosis, cancer treatment and medication discovery. However, most data in the gene expression profile are not able to make a contribution to cancer classification and prediction. Hence, it is important to find the key genes that are relevant. An entropy-based improved k-top scoring pairs (TSP)?(Ik-TSP) method was presented in this?study?for?the?classification and prediction of human cancers based on gene-expression data. We compared Ik-TSP classifiers with 5 different machine learning methods and the k-TSP method based on 3 different feature selection methods on 9 binary class gene expression datasets and 10 multi-class gene expression datasets involving human cancers. Experimental results showed that the Ik-TSP method had higher accuracy. The experimental results also showed that the proposed method can effectively find genes that are important for distinguishing different cancer and cancer subtype.
机译：基于基因表达谱的不同癌症的分类和预测对于癌症诊断，癌症治疗和药物发现非常重要。然而，基因表达谱中的大多数数据不能对癌症的分类和预测做出贡献。因此，重要的是找到相关的关键基因。本研究提出了一种基于熵的改进的k-top得分对（TSP）？（Ik-TSP）方法，用于基于基因表达数据对人类癌症进行分类和预测。我们在涉及人类癌症的9个二元类基因表达数据集和10个多类基因表达数据集上，将Ik-TSP分类器与5种不同的机器学习方法和基于3种不同特征选择方法的k-TSP方法进行了比较。实验结果表明，Ik-TSP方法具有较高的准确性。实验结果还表明，该方法可以有效地发现对于区分不同癌症和癌症亚型重要的基因。

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