邻近规范矩阵因子化为lncRNA-疾病关联识别的因子化
1Major of Big Data Convergence, Division of Data Information Science, Pukyong National University, Busan 48513, Republic of Korea.
International journal of molecular sciences
|May 14, 2025
概括
这项研究介绍了NRMFLDA,这是一种用于预测与疾病相关的长非编码RNA (lncRNAs) 的新型模型. 该模型准确地识别了与疾病相关的 lncRNA,有助于生物标志物发现和推进诊断.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- 长非编码RNA (lncRNAs) 在生物过程和人类疾病中起着关键作用.
- 识别 lncRNA-疾病关联对于疾病生物标志物发现和治疗开发至关重要.
研究的目的:
- 开发一种有效的计算模型来推断与疾病相关的 lncRNAs.
- 为了提高 lncRNA-疾病关联预测的准确性和可靠性.
主要方法:
- 提出了一个基于推系统的模型,命名为NRMFLDA.
- 使用矩阵因子化与疾病社区规范化.
- 员工一次性休假和绩效评估的五倍交叉验证.
主要成果:
- NRMFLDA实现了高性能,其AUC分数为0.9143和0.8993. 这两种分数均为0.9143和0.8993.
- 在预测 lncRNA - 疾病关联方面表现优于以前建立的四种模型.
- 在识别与疾病相关的 lncRNA 方面表现出强度和有效性.
结论:
- NRMFLDA 提供了一种创新的方法来发现 lncRNA 与疾病的关联.
- 该模型可以为识别新型疾病生物标志物做出重大贡献.
- 通过这项研究,预计诊断和治疗策略将取得进展.
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