Mesa-Rodríguez Ania, Gonzalez Augusto, Estevez-Rams Ernesto, Valdes-Sosa Pedro A
The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Sciences and Technology of China, Chengdu 610054, China.
Facultad de Matemática, Universidad de La Habana, San Lazaro y L, La Habana 10400, Cuba.
Entropy (Basel). 2022 Nov 29;24(12):1744. doi: 10.3390/e24121744.
The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity-entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.
海量基因表达数据的可用性在如何处理、加工和提取有用信息方面一直具有挑战性。在此，我们描述了使用熵度量作为区分标准，利用基因表达水平的完整数据集对癌症进行分析。这些方法被应用于对13种肿瘤的肿瘤样本和正常样本进行分类，成功率很高。利用按通路排序的基因表达，可得到复杂性-熵图。该图谱能够对肿瘤样本和正常样本进行聚类，在所研究的13种癌症类型中，有9种成功率较高。使用信息距离进行的进一步分析也显示出良好的区分能力，但更重要的是，能够区分不同的癌症类型。总之，我们的结果使得无需识别相关基因或强加特定癌症模型就能对组织进行分类。所使用的程序可以扩展到超出报告结果的分类问题上。
