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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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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相关实验视频

Updated: May 17, 2025

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

使用基于深度学习的基因表达数据对肺癌严重程度的分类.

Ali Bou Nassif¹, Nour Ayman Abujabal², Aya Alchikh Omar²

¹Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, P.O Box: 27272, Sharjah, UAE. anassif@sharjah.ac.ae.

BMC medical informatics and decision making

|May 14, 2025

概括

这项研究引入了一个深度学习 (DL) 模型,一个卷积神经网络 (CNN),用于分类肺癌的阶段. 该模型在识别肺腺癌 (LUAD) 和肺状细胞癌 (LUSC) 方面取得了很高的准确性.

关键词:

分类分类分类分类.深度学习是一种深度学习.功能选择功能选择基因表达基因表达基因表达卢阿德 (Luad) 的意思是说.在LUSC中使用LUSC.肺癌是一种肺癌.

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Last Updated: May 17, 2025

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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:

肺癌是全球癌症相关死亡的主要原因之一.
机器学习 (ML) 和深度学习 (DL) 在癌症检测和分类方面表现有前途.
基因表达数据为了解和诊断肺癌亚型提供了潜力.

研究的目的:

开发和评估一个深度学习 (DL) 模型来分类肺癌的阶段.
专门解决阶级不平衡和过度适应肺癌基因表达数据集的挑战.
用基因数据提高肺癌亚型分类的准确性.

主要方法:

一个卷积神经网络 (CNN) 模型被设计用于肺癌阶段分类.
使用F-test特征选择方法来优化模型.
使用了肺腺癌 (LUAD) 和肺状细胞癌 (LUSC) 的基因表达数据集.
进行了医学和实验分析,以减轻数据集挑战.

主要成果:

优化的CNN模型实现了肺癌亚型的高分类准确性.
对肺腺癌 (LUAD) 获得了大约93.94%的准确性.
对于肺状细胞癌 (LUSC) 获得了大约88.42%的准确性.

结论:

深度学习,特别是CNN,可以有效地使用基因表达数据对肺癌的阶段进行分类.
在这种情况下,F测试特征选择方法有利于提高DL模型性能.
提出的方法为肺癌诊断和研究提供了一个有前途的工具.