DeepdlncUD:通过深度学习预测小分子抑制剂的调节类型,通过深度学习调节lncRNA表达
Jianfeng Sun1, Shuyue Si2, Jinlong Ru3
1Botnar Research Centre, University of Oxford, Headington, Oxford, OX3 7LD, UK.
Computers in biology and medicine
|July 14, 2023
概括
DeepdlncUD是一个新的计算工具,可以预测小分子如何调节长非编码RNA (lncRNA) 表达. 这种方法有助于发现潜在的 lncRNA向疾病的药物.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 基因组学就是基因组学.
背景情况:
- 小分子-lncRNA相互作用对于治疗开发至关重要.
- 现有的计算工具缺乏预测小分子介导的lncRNA表达调节的能力.
研究的目的:
- 开发和验证DeepdlncUD,这是一个新的计算工具,用于预测小分子诱导的lncRNA表达的上调或下调.
- 评估DeepDlncUD的性能与现有方法相比,并在病理学上下文.
主要方法:
- DeepdlncUD集成了来自九个深度学习算法的预测.
- 该模型在广泛的小分子-lncRNA调节对数据集上进行了训练和测试,包括序列,表示和生理化学特征.
- 使用AUC,AUCPR,F1得分和贾卡德指数来评估性能.
主要成果:
- 在测试组中,DeepdlncUD显著优于DeepsmirUD程序,实现了卓越的指标,包括AUC为0.674和AUCPR为0.722.
- 在疾病相关的lncRNAs的背景下,DeepdlncUD显示了0.700的准确性.
- 根据连接性得分,大约50%的小分子被准确地确定为 lncRNA 调节疾病的潜在药物.
结论:
- DeepdlncUD提供了一种强大而高效的计算方法,用于预测小分子-lncRNA调节相互作用.
- 该工具通过识别针对lncRNAs的潜在小分子治疗方法来促进大规模的药物发现.
- DeepdlncUD是公开可用的,以帮助研究人员推进基于 lncRNA 的治疗策略.
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