Statistical Learning Analysis of Thyroid Cancer Microarray Data

The classification of human cancers constitutes to date a significant challenge in the context of microarray data analysis. The discovery of gene hallmarks for biological processes involves the examination of large gene expression matrices in a broad and massively parallel manner. In this article, a comprehensive and comparative analysis of thyroid cancer datasets is presented, including stages for feature selection, hypothesis testing, and classification. Also, datasets are integrated, and results for this integration are reported and analyzed. To conclude, text mining is used to investigate some biological information regarding the main resulting characteristic genes. Some genes found during the research, HINT3 in particular, appear to be worth to be further studied.

Automated summary

This article presents a comprehensive and comparative analysis of thyroid cancer datasets, including stages for feature selection, hypothesis testing, and classification. The results suggest that some genes, especially the HINT3 gene, are worth further investigation.