使用基于转移学习和层次注意力网络机制的双向长期短期记忆模型预测胆囊托基宁-分泌
Jing Liu1, Pu Chen1, Hongdong Song2
1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Biomolecules
|September 28, 2023
概括
这项研究引入了一种新的计算方法,可以有效预测胆囊托基宁 (CCK) 分泌,这对于治疗肥胖和帕金森病等疾病至关重要. 先进的模型实现了高精度,为传统实验提供了更快的替代方案.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 酸科学 酸科学
背景情况:
- 胆囊托基宁 (CCK) 调节和感,并具有神经营养和抗炎性质.
- CCK的治疗潜力涵盖肥胖症,帕金森病,胰腺癌和胆管癌.
- 目前用于识别新型CCK分泌的实验方法资源密集且耗时.
研究的目的:
- 开发一种高效的计算方法,用于预测新型CCK分泌.
- 用转移学习和数据增强来解决预测中的数据稀缺挑战.
- 建立一个强大的模型来加速发现CCK分泌的发现.
主要方法:
- 利用转移学习与SMILES计数相结合,用于数据增强.
- 开发了一种融合模型,集成层次注意网络 (HAN) 和双向长短期记忆 (BiLSTM).
- 采用该模型从序列中提取全面特征以进行预测.
主要成果:
- 实现了95.99%的平均精度和98.07%的曲线下面积 (AUC).
- 与现有方法相比,在准确性和稳定性方面表现出卓越的性能.
- 成功地预测了CCK分泌的高效率.
结论:
- 拟议的计算方法显著提高了CCK分泌的预测.
- 这种方法为传统的实验查提供了一个具有成本效益和时间效率的替代方案.
- 该方法对初步查和加速治疗的发现具有前途.
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