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lncRNA - Long Non-coding RNAs02:39

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Modeling Breast Cancer via an Intraductal Injection of Cre-expressing Adenovirus into the Mouse Mammary Gland
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一个基因组驱动的基于人工智能的模型对乳腺侵入性叶状癌进行了分类,并发现了CDH1非激活机制.

Fresia Pareja1, Higinio Dopeso1, Yi Kan Wang2

  • 1Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.

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这项研究开发了一个人工智能 (AI) 模型用于乳腺癌诊断,使用基因突变作为基本事实. 人工智能模型准确地识别了侵入性叶叶癌 (ILC),并发现了驱动癌症的新遗传机制.

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科学领域:

  • 病理学 病理学 病理学
  • 基因组学就是基因组学.
  • 人工智能的人工智能

背景情况:

  • 癌症诊断中的人工智能 (AI) 通常依赖主观组织学特征进行训练.
  • 开发客观的基础真相对于病理学中强大的AI模型开发至关重要.

研究的目的:

  • 开发一种人工智能模型来诊断侵袭性叶叶癌 (ILC),使用CDH1双基突变作为基因基础真理.
  • 探索乳腺瘤中其他CDH1无活化机制.
  • 为了建立一个框架,利用直角地面真理在AI开发全幻灯片成像.

主要方法:

  • 开发了一个人工智能模型,在乳腺新生瘤的全幻灯片图像上进行训练.
  • 利用了CDH1双基突变,作为ILC的病理标志,作为基因基础真理.
  • 验证了人工智能模型在内部和外部队列上的表现.

主要成果:

  • 人工智能模型在预测CDH1双基突变 (0.95) 和诊断ILC (0.96) 方面取得了很高的准确性.
  • 在74%缺乏预期突变的样本中确定了替代的CDH1无活化机制.
  • 在验证队列中表现出强大的诊断准确性 (0.95和0.89).
  • 关联AI模型的潜伏特征与可解释的基因病理特征.

结论:

  • 训练有素的基因基础真相的人工智能模型可以稳定地对ILC进行分类,并发现新的生物见解.
  • 这种方法为在病理学诊断AI中对直角地面真理的利用提供了基础.
  • 具有强烈的基因型-表型相关性的遗传变化可以增强用于癌症诊断和发现的AI开发.