DTIP-WINDGRU是一种新的药物向相互作用预测,使用风增强的封闭反复单元
Kavipriya Gananathan1, D Manjula2, Vijayan Sugumaran3,4
1School of Computer Science Engineering (SCOPE), Vellore Institute of Technology, Chennai, 600127, India. kavipriya.g@vit.ac.in.
BMC bioinformatics
|July 20, 2025
概括
这项研究介绍了DTIP-WINDGRU,这是一种用于预测药物向相互作用 (DTI) 的新型计算智能模型. 该模型在标记和未标记样本中准确识别了DTI,性能优于现有方法.
科学领域:
- 计算智能是一种计算智能.
- 机器学习 机器学习
- 药物发现 药物发现
背景情况:
- 药物向相互作用 (DTI) 识别对于药物发现至关重要,但通过实验室测试耗时.
- 计算智能 (CI) 提供了必要的自动预测工具.
- 挑战包括有限的数据,缺乏负样本和复杂的现有模型.
研究的目的:
- 开发一个新的DTI预测模型,DTIP-WINDGRU (用风增强GRU进行药物向相互作用预测).
- 在标记和未标记样本中准确确定DTI,超越传统方法.
- 利用先进的机器学习 (ML) 和深度学习 (DL) 进行有效的DTI预测.
主要方法:
- DTIP-WINDGRU模型包括预处理和类标签.
- 药物对药物 (D-D) 和目标对目标 (T-T) 相互作用初始化GRU模型权重.
- 风驱动优化 (WDO) 算法用于GRU模型中的最佳超参数选择.
主要成果:
- DTIP-WINDGRU模型展示了有效的DTI预测能力.
- 该模型准确地识别了标记和未标记数据集中的相互作用.
- 对比分析证实了比现有的DTI预测技术更高的性能.
结论:
- DTIP-WINDGRU模型为DTI预测提供了一个强大的解决方案.
- 广泛的实验验证了该模型在四个数据集中的有效性.
- 该研究强调了DTIP-WINDGRU与当前方法相比的优越性能.
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