LDAEXC:用深度自编码器和XGBoost分类器进行LncRNA疾病关联预测
1College of Information Science and Engineering, Hunan Normal University, Changsha, China.
Interdisciplinary sciences, computational life sciences
|June 12, 2023
概括
这项研究介绍了LDAEXC,这是一种用于预测长非编码RNA (lncRNA) -疾病关联的新型计算框架. LDAEXC能够准确地识别潜在的与疾病相关的 lncRNA,帮助诊断和治疗疾病.
科学领域:
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 长非编码RNA (lncRNAs) 在人类复杂疾病中起着至关重要的作用.
- 识别 lncRNA-疾病关联对于疾病诊断,预后和治疗至关重要.
- 识别这些关联的传统实验方法耗时且昂贵.
研究的目的:
- 开发一个准确的计算框架,LDAEXC,用于推断lncRNA与疾病的关联.
- 改进现有的计算方法来预测与疾病相关的 lncRNAs.
主要方法:
- LDAEXC集成了lncRNAs和疾病的多重相似性视图,以构建特征.
- 一个深度的自动编码器用于构建特征的维度减小.
- 使用XGBoost分类器来预测lncRNA与疾病的关联.
主要成果:
- 在五倍交叉验证实验中,LDAEXC取得了高的曲线下面积 (AUC) 评分 (例如,在一个数据集上为0.9676 ± 0.0043).
- 在多个数据集中,性能明显超过了其他先进的计算方法.
- 结肠癌和乳腺癌的案例研究表明了该框架的实际实用性和预测能力.
结论:
- LDAEXC提供了一种准确有效的方法来推断lncRNA与疾病的关联.
- 该框架有望推动复杂疾病机制和治疗策略的研究.
- LDAEXC的卓越表现突显了生物信息学深度学习和组合方法的潜力.
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