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引入特征识别和改进引擎 (FIRE),用于识别一致和信息丰富的基因签名

Shivam Kumar1, Devvrat Pandey1, Samrat Chatterjee1

  • 1Complex Analysis Group, Computational and Mathematical Biology Centre, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, 121001, India.

Computational biology and chemistry
|November 2, 2025
PubMed
概括

我们开发了一个机器学习框架FIRE,用于识别质母细胞瘤等疾病的分子特征. FIRE成功地确定了33个区分质母细胞瘤的基因,为癌症研究和诊断提供了有前途的工具.

关键词:
数据合并数据的合并.基因表达数据 基因表达数据质母细胞瘤 (glioblastoma) 是一个机器学习是机器学习.分子签名 分子签名

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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