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转移学习与BioBERT嵌入用于lncRNA-疾病协会预测

IEEE transactions on computational biology and bioinformatics

|November 5, 2025

概括

本研究介绍了TBLDA,一种使用转移学习来预测长非编码RNA (lncRNA) 和疾病关联的计算框架. 通过利用语义疾病表示和预训练模型,TBLDA提高了准确性,优于现有的方法.

科学领域:

基因组学和生物信息学
分子生物学分子生物学
计算生物学计算生物学

背景情况:

长非编码RNAs (lncRNAs) 调节基因表达,并与癌症和神经系统疾病等疾病有关.
了解ncRNA与疾病的联系对于开发生物标志物和疗法至关重要.
对于 lncRNA-疾病关联分析的传统实验方法在可扩展性和成本上是有限的.

研究的目的:

开发一种新的计算框架,TBLDA,用于预测长非编码RNA与疾病的关联.
利用转移学习和先进的自然语言处理技术来提高预测准确度.
提供一种强大的工具来识别与疾病相关的 lncRNA,以帮助医学研究.

主要方法:

开发了TBLDA,一种基于转移学习的框架,利用BERT衍生的嵌入用于疾病术语语义.
集成了一个预先训练的miRNA-疾病关联模型来转移知识.
员工休假一次交叉验证 (LOOCV) 和5倍交叉验证 (5倍简历) 用于绩效评估.

主要成果:

TBLDA实现了高预测性能,AUC分数为0.9801 (LOOCV) 和0.9721 (CV为5倍).
该框架显著超过了五种竞争基准模型.
证明了转移学习策略在提高lncRNA疾病预测方面的有效性.

结论:

TBLDA是一种强大而有效的计算工具,用于预测 lncRNA 与疾病的关联.
转移学习方法提高了预测准确性,超越了现有的方法.
通过识别潜在的与疾病相关的lncRNAs,TBLDA可以补充实验研究,促进进一步的医学研究.

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