带有范围限制的异质图推理L2,1-小分子-miRNA关联预测的协作矩阵因子化.
Shudong Wang1, Tiyao Liu1, Chuanru Ren1
1College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China.
Computational biology and chemistry
|April 27, 2024
概括
本研究介绍了HGIRCLMF,这是一种通过改进相似度指标和在异质网络上使用矩阵分解来预测小分子-微RNA关联的新方法. 这种方法提高了确定疾病治疗潜在药物点的准确性.
科学领域:
- 计算生物学和生物信息学
- 基因组学和分子生物学
- 药理学和药物发现
背景情况:
- 微RNA (miRNA) 是基因表达和生物过程的关键调节者,使它们成为疾病治疗中小分子 (SM) 药物的关键标.
- 预测SM-miRNA关联对于药物发现至关重要,但现有的方法在稀疏的关联网络和不精确的相似度量方面扎.
- 异质图推理是一种常见的方法,但它的有效性受到数据稀疏性和不准确的相似性计算的限制.
研究的目的:
- 开发一种先进的计算方法,HGIRCLMF,用于准确预测潜在的小分子-微RNA关联.
- 解决现有的SM-miRNA关联预测模型中稀疏性和不精确相似度指标的局限性.
- 通过改进关联推断,提高识别新型治疗点的可靠性和准确性.
主要方法:
- 计算了小分子 (SMs) 和miRNAs的多源相似性,将它们整合到全面的相似度量.
- 采用一种新型范围受约束的L2,1-协作矩阵因子化 (RCLMF) 模型来解决矩阵稀疏性并增强SM-miRNA边缘强度.
- 使用处理的生物数据构建了一个异质网络,并应用HGIRCLMF模型推断未知关联得分.
主要成果:
- 拟议的HGIRCLMF方法在预测SM-miRNA关联方面取得了卓越的性能,在交叉验证实验中曲线下的最高区域证明了这一点.
- 在两个不同的数据集上,HGIRCLMF的性能超过了六种最先进的计算方法,这表明其预测准确度提高了.
- 案例研究证实了HGIRCLMF在现实世界药物发现场景中的实际适用性和预测能力.
结论:
- 在SM-miRNA关联预测中,HGIRCLMF有效地克服了数据稀疏性和不准确的相似度指标的挑战.
- 该方法提供了一个强大而准确的计算工具,用于识别潜在的SM-miRNA关联,有助于药物向发现.
- 这项研究为生物信息学和计算药物发现领域的重大进步做出了贡献.
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