iEnhancer-DS:基于注意力的改进密度网,用于识别增强剂及其强度
Yongxian Fan1, Chen Wang1, Guicong Sun1
1Guilin University of Electronic Technology, School of Computer Science and Information Security, Guilin, 541004, Guangxi, China.
Computational biology and chemistry
|May 11, 2025
概括
本研究介绍了iEnhancer-DS,这是一个深度学习框架,用于准确识别DNA增强剂及其表达强度. 这种新方法显著提高了基因调节分析现有的计算方法的性能.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 增强剂是关键的DNA元素,通过转录因子结合来调节基因表达.
- 精确识别和强度预测增强剂对于理解基因调节至关重要.
- 目前的实验方法昂贵且耗时;现有的计算方法在性能和编码复杂性方面存在局限性.
研究的目的:
- 开发一种高效准确的计算方法,用于增强剂识别和强度分类.
- 提出基于深度学习的多任务框架iEnhancer-DS,解决当前方法的局限性.
主要方法:
- 利用一热编码和核酸化学特性 (NCP) 进行DNA序列特征嵌入.
- 采用了改进的DenseNet模块来学习高阶隐性特征.
- 集成了用于动态特征加权的自我注意机制和用于预测的多层感知子 (MLP).
主要成果:
- 在增强器识别和强度预测任务中,iEnhancer-DS实现了最先进的性能.
- 与现有方法相比,在准确性 (ACC) 和马修斯相关系数 (MCC) 中取得了显著的改进.
- 增强剂的识别显示ACC和MCC分别增加了4.03%和8.47%.
- 增强剂强度预测显示ACC和MCC分别增加了1.40%和3.81%.
- 使用t-SNE的可解释分析可视化了模型的作用机制.
结论:
- iEnhancer-DS提供了一个强大而准确的深度学习解决方案,用于增强器识别和强度预测.
- 该框架为推进基因调节机制研究提供了有价值的工具.
- 该研究提供了公开可用的代码和数据,用于可复制性和进一步开发.
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