DL-TCNN:基于深度学习的时间卷积神经网络,用于预测构造性B细胞表位
Pratik Angaitkar1, Rekh Ram Janghel1, Tirath Prasad Sahu1
1Department of Information Technology, National Institute of Technology, Raipur, G.E. Road, Raipur, C.G. 492010 India.
3 Biotech
|August 14, 2023
概括
一个新的基于深度学习的时间卷积神经网络 (DL-TCNN) 模型准确地预测了构造性B细胞表位 (CBCE). 这种先进的框架为疫苗设计和药物发现提供了更高的准确性.
科学领域:
- 免疫信息学是指免疫信息学.
- 计算生物学 计算生物学
- 机器学习在医学中的应用
背景情况:
- 预测形态B细胞表位 (CBCE) 对于疫苗设计,药物开发和疾病诊断至关重要.
- 传统的实验室方法耗时且昂贵,推动了诸如机器学习 (ML) 等计算方法的采用.
- 现有的ML方法在实现CBCE预测的高精度方面面临挑战.
研究的目的:
- 引入一种新的基于深度学习的时间卷积神经网络 (DL-TCNN) 框架,用于增强CBCE预测.
- 利用深度学习的优势,特别是混合1D-CNN和TCN架构,以提高预测性能.
- 解决现有ML模型的局限性,提高CBCE预测的准确性.
主要方法:
- 从抗原序列中提取了物理化学特征.
- 为了减轻阶级不平衡问题,采用了合成少数群体过量采样技术 (SMOTE).
- 开发和训练了一种混合DL-TCNN模型,将1D-CNN和TCN与因果卷曲和扩张结合起来.
主要成果:
- DL-TCNN模型在培训,验证和测试数据集上表现出高的性能.
- 在训练组中获得了94.44%的准确性和0.989 AUC.
- 在测试组中报告了85.10%的准确性和0.855 AUC,优于现有的CBCE预测方法.
结论:
- 拟议的DL-TCNN框架代表了计算CBCE预测的重大进步.
- 该模型的卓越性能突显了深度学习的潜力,特别是TCN架构在免疫信息学中的潜力.
- 这种方法为疫苗设计和治疗开发中的应用提供了更准确和更有效的工具.
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