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Updated: Jun 24, 2025

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma

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Published on: March 11, 2014

一种深度学习方法用于分类HNSCC和HPV患者使用单细胞转录组学.

Akanksha Jarwal¹, Anjali Dhall¹, Akanksha Arora¹

¹Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, India.

Frontiers in molecular biosciences

|June 14, 2024

概括

这项研究开发了用于早期检测头部和部状细胞癌 (HNSCC) 和HPV状态分类的机器学习模型. 人工神经网络实现了高精度,有助于癌症诊断和管理.

关键词:

在HNSCC中,我们可以看到.分类模型的分类模型.深度学习是一种深度学习.基因生物标志物基因生物标志物机器学习是机器学习.一个单细胞的转录组学.

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相关实验视频

Last Updated: Jun 24, 2025

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Published on: October 25, 2018

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

头部和部状细胞癌 (HNSCC) 是一种全球普遍存在的癌症.
早期发现HNSCC至关重要,但由于昂贵和侵入性的方法,具有挑战性.
目前对HNSCC的诊断方法需要在成本效益和患者侵入性方面进行改进.

研究的目的:

开发和评估用于HNSCC检测的机器学习和深度学习模型.
将HNSCC样本分为HPV阳性 (HPV+) 和HPV阴性 (HPV-) 类别.
使用转录组学数据识别与HNSCC相关的关键基因及其HPV状态.

主要方法:

使用单细胞转录组学数据 (GSE181919数据集),包括HNSCC和正常样本.
应用特征选择 (mRMR) 来识别重要的基因和基因本体学 (GO) 丰富分析.
开发并验证了分类模型,包括人工神经网络,80%的培训和20%的验证数据分割.

主要成果:

一个人工神经网络模型在验证集上实现了HNSCC分类的0.91的AUROC.
同一个模型显示,对HPV+和HPV-HNSCC患者进行分类的AUROC为0.83.
基因丰富分析表明,选择的基因主要参与结合和催化活动.

结论:

开发了准确的机器学习模型来检测HNSCC和确定HPV状态.
在Python中创建了一个用户友好的软件包,用于HNSCC预测和HPV状态识别.
开发的工具可以在线访问,为HNSCC诊断提供一种潜在的非侵入性方法.