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相关概念视频

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
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相关实验视频

Updated: Jan 10, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

475

综合性长非编码RNA分析和宫癌的复发预测使用复发神经网络.

Geeitha Senthilkumar1, Renuka Pitchaimuthu1, Prabu Sankar Panneerselvam2

  • 1Department of Information Technology, M. Kumarasamy College of Engineering, Thalavapalayam, Karur 639113, Tamil Nadu, India.

Diagnostics (Basel, Switzerland)
|November 27, 2025
PubMed
概括
此摘要是机器生成的。

一个新的9个长的非编码RNA (lncRNA) 签名,结合深度学习,有效地根据患者患子宫癌复发风险进行分层,提高了预后准确性.

关键词:
生物标志物生物标志物长非编码RNA是什么意思预后 预后 预后再发性子宫癌的发生.经常性的神经网络.

相关实验视频

Last Updated: Jan 10, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

475

科学领域:

  • 在瘤学瘤学.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 再发性子宫癌对患者的生存构成重大威胁.
  • 准确的预后模型对于识别高风险个体至关重要.
  • 目前的模型需要通过集成的临床和分子数据来增强.

研究的目的:

  • 将临床数据与GSE44001数据集集集成,以确定宫癌复发风险因素.
  • 将患者分为高风险,中风险和低风险组.
  • 为了识别长非编码RNA (lncRNA) 基因签名的复发性宫癌.

主要方法:

  • 利用来自NCBI GEO数据库的GSE44001数据集,专注于138名复发性宫癌患者.
  • 使用GENCODE注释工具过和分析长非编码RNA (lncRNAs).
  • 采用最小绝对收缩选择运算器 (LASSO) 来根据lncRNA表达和系数进行特征选择和风险值分配.

主要成果:

  • 一个复发神经网络 (RNN) 长期短期记忆模型显示出预后价值,高风险患者的无复发生存时间较短 (p < 0.05).
  • 特定的lncRNA标记物 (ATXN8OS,C5orf60,INE1) 与疾病复发有关.
  • 其他标记物 (KCNQ1DN,LOH12CR2,RFPL1S,KCNQ1OT1,EMX2OS) 与早期或中等阶段的诊断相关.

结论:

  • 一个九个lncRNA签名显示出预测宫癌复发的巨大潜力.
  • 这种 lncRNA 签名与深度学习的整合提供了一个强大的风险分层方法.
  • 这种方法可以帮助识别可能受益于加强监测或治疗的患者.