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相关概念视频

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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相关实验视频

Updated: Jul 7, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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GSLCDA:一种无监督的深度图形结构学习方法,用于预测circRNA-疾病关联.

Lei Wang, Zheng-Wei Li, Zhu-Hong You

    IEEE journal of biomedical and health informatics
    |December 21, 2023
    PubMed
    概括

    这项研究引入了GSLCDA,这是一种用于预测circRNA-疾病关联 (CDA) 的新型无监督方法. GSLCDA增强了图形结构的学习,以提高对复杂疾病的潜在CDA识别的准确性.

    科学领域:

    • 基因组学和生物信息学
    • 计算生物学 计算生物学
    • 分子医学是分子医学.

    背景情况:

    • 循环RNAs (circRNAs) 越来越多地被认为是它们在细胞过程和疾病发病过程中的角色.
    • 准确预测circRNA疾病关联 (CDAs) 对于开发新型治疗策略至关重要.
    • 现有的CDA预测方法与杂的图形结构作斗争,限制了它们的性能.

    研究的目的:

    • 开发一种无监督的深度图形结构学习方法,GSLCDA,用于预测潜在的circRNA疾病关联 (CDA).
    • 解决现有方法的局限性,这些方法严重依赖图形网络,易受噪声连接的影响.

    主要方法:

    • 整合多源circRNA和疾病数据,构建一个异质网络.
    • 无监督的深度图形结构学习来增强网络拓学和发现基本特征.
    • 应用图形空间敏感的k-最接近邻居 (KNN) 算法用于潜伏的CDA识别.

    主要成果:

    • 在基准数据集上,GSLCDA获得了92.67%的准确性和0.9279 AUC.
    • 该方法在独立数据集上表现出了卓越的性能.
    • 乳腺癌,结肠直肠癌和肺癌的案例研究显示,预测CDA的验证率很高.

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    结论:

    • GSLCDA有效地预测了潜在的circRNA疾病关联.
    • 该方法为人类复杂疾病的诊断和治疗提供了新的视角.
    • GSLCDA为CDA预测提供了一个强大的方法,克服了以前基于图表的方法的局限性.