Ghosh Anupam, De Rajat K
Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, India,
J Biosci. 2015 Oct;40(4):741-54. doi: 10.1007/s12038-015-9557-x.
In this article, we have used an index, called Gaussian fuzzy index (GFI), recently developed by the authors, based on the notion of fuzzy set theory, for validating the clusters obtained by a clustering algorithm applied on cancer gene expression data. GFI is then used for the identification of genes that have altered quite significantly from normal state to carcinogenic state with respect to their mRNA expression patterns. The effectiveness of the methodology has been demonstrated on three gene expression cancer datasets dealing with human lung, colon and leukemia. The performance of GFI is compared with 19 exiting cluster validity indices. The results are appropriately validated biologically and statistically. In this context, we have used biochemical pathways, p-value statistics of GO attributes, t-test and zscore for the validation of the results. It has been reported that GFI is capable of identifying high-quality enriched clusters of genes, and thereby is able to select more cancer-mediating genes.
在本文中，我们使用了作者最近基于模糊集理论概念开发的一个名为高斯模糊指数（GFI）的指标，用于验证应用于癌症基因表达数据的聚类算法所得到的聚类。然后，GFI被用于识别那些在mRNA表达模式方面从正常状态到致癌状态有显著变化的基因。该方法的有效性已在处理人类肺癌、结肠癌和白血病的三个基因表达癌症数据集上得到证明。GFI的性能与19个现有的聚类有效性指标进行了比较。结果在生物学和统计学上都得到了适当的验证。在这种情况下，我们使用生化途径、GO属性的p值统计、t检验和z分数来验证结果。据报道，GFI能够识别高质量的基因富集聚类，从而能够选择更多的癌症介导基因。
