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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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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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相关实验视频

Updated: Jan 17, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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在图表上进行自我监督学习可以预测非编码RNA和疾病关联.

Qingwen Wu1, Sujuan Tang2

  • 1Department of Data Center, Affiliated Hospital of Jining Medical University, Jining, China.

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|January 14, 2026
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概括
此摘要是机器生成的。

本研究介绍了SSLGRDA,这是一种结合自主监督学习和机器学习的新方法,用于准确预测非编码RNA疾病关联 (RDA). 这种方法提高了预测准确性和概括性,以获得更好的癌症治疗策略.

关键词:
协会预测 协会预测相反的学习学习.图表神经网络的神经网络非编码RNA疾病自主监督学习学习

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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.
  • 基因组学就是基因组学.

背景情况:

  • 非编码RNAs (ncRNAs) 是癌症发展和进展的关键调节者.
  • 准确识别ncRNA-疾病关联 (RDAs) 对于开发向癌症疗法至关重要.
  • 现有用于RDA预测的图形卷积网络方法面临数据噪声和不良概括性的挑战.

研究的目的:

  • 为预测ncRNA-疾病关联 (RDAs) 开发一个强大的和可概括的框架.
  • 为了提高确定潜在的ncRNA-疾病关系的准确性,以改善治疗干预措施.

主要方法:

  • 提出SSLGRDA,这是一个集成图形自主监督学习和机器学习的新方案.
  • 构建了包含已知的RDA和节点相似性的异质和同质图.
  • 采用多种对比或生成策略进行图形自我监督学习,以提取强大的ncRNA和疾病嵌入.
  • 利用机器学习来预测潜在的RDA概率.

主要成果:

  • 在9个ncRNA疾病数据集中,SSLGRDA展示了强大的概括能力.
  • 提出的方法超过了几种最先进的RDA预测技术.
  • 案例研究证实SSLGRDA在发现新型ncRNA疾病关联方面的有效性.

结论:

  • SSLGRDA为预测ncRNA与疾病的关联提供了一种强大而可通用的方法.
  • 该方法在推进精确瘤学和治疗策略设计方面具有重大潜力.
  • 通过强大的节点嵌入,SSLGRDA有效地提高了预测能力和模型概括性.