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Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm.

Yang JiangPeiwei ZhangLi-Peng LiYi-Chun HeRu-jian GaoYu-Fei Gao
Published in: BioMed research international (2015)
Thyroid cancer is a typical endocrine malignancy. In the past three decades, the continued growth of its incidence has made it urgent to design effective treatments to treat this disease. To this end, it is necessary to uncover the mechanism underlying this disease. Identification of thyroid cancer-related genes and chemicals is helpful to understand the mechanism of thyroid cancer. In this study, we generalized some previous methods to discover both disease genes and chemicals. The method was based on shortest path algorithm and applied to discover novel thyroid cancer-related genes and chemicals. The analysis of the final obtained genes and chemicals suggests that some of them are crucial to the formation and development of thyroid cancer. It is indicated that the proposed method is effective for the discovery of novel disease genes and chemicals.
Keyphrases
  • bioinformatics analysis
  • machine learning
  • genome wide
  • deep learning
  • gene expression
  • risk factors
  • dna methylation
  • high throughput
  • single cell