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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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相关实验视频

Updated: Jun 9, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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circ2DGNN:通过基于变压器的图形神经网络进行circRNA-疾病关联预测.

Keliang Cen, Zheming Xing, Xuan Wang

    IEEE/ACM transactions on computational biology and bioinformatics
    |October 30, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了circ2DGNN,这是一种用于预测循环RNA (circRNA) 和疾病关联的新型计算模型. 通过将多种生物分子相互作用集成到异质网络中,circ2DGNN增强了疾病机制的理解和治疗策略的开发.

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    A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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    In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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    科学领域:

    • 生物信息学是一种生物信息学.
    • 计算生物学 计算生物学
    • 基因组学就是基因组学.

    背景情况:

    • 了解循环RNA (circRNA) 和疾病关联对于阐明疾病机制和治疗开发至关重要.
    • 当前的计算方法往往间接考虑生物分子的影响,限制了预测circRNA-疾病相互作用的准确性.
    • 需要整合多样化的生物分子数据的综合方法来改善circRNA-疾病关联预测.

    研究的目的:

    • 开发一种新的计算模型,circ2DGNN,用于预测circRNA与疾病的关联.
    • 为了利用异构的图形神经网络,并纳入各种生物分子相互作用数据.
    • 提高circRNA疾病关联预测的准确性和全面性.

    主要方法:

    • 构建了一个包括人类circRNAs,疾病和其他生物分子相互作用在内的综合异质网络.
    • 开发了circ2DGNN,一种利用图形表示学习的异质图形神经网络模型.
    • 采用了类似变压器的架构,对消息传播和聚合给予异质的关注,并结合了剩余连接.

    主要成果:

    • circ2DGNN有效地集成异质网络数据,用于下游链接预测.
    • 该模型在测试数据集上展示了与现有的最先进方法相比更高的性能.
    • 通过五倍交叉验证优化模型超参数进行微调.

    结论:

    • circ2DGNN提供了一种强大的新方法,通过直接利用异质网络信息来预测circRNA与疾病的关联.
    • 该模型能够结合多种生物分子相互作用的能力提高了对疾病机制的理解.
    • 这项工作为推进研究circRNA相关疾病和潜在疗法的研究提供了宝贵的工具.