NeuroCNN_GNB:基于卷积神经网络和高斯的天真贝叶斯模型来预测神经的一组模型
Di Liu1, Zhengkui Lin1, Cangzhi Jia2
1Information Science and Technology College, Dalian Maritime University, Dalian, China.
Frontiers in genetics
|August 14, 2023
概括
这项研究介绍了NeuroCNN_GNB,这是一种用于准确预测神经的机器学习工具. 它通过整合多个卷积神经网络模型和提供特征解释,优于现有方法.
科学领域:
- 神经科学是一个神经科学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 神经提供了比经典的神经递质更大的化学信息和受体选择性,导致副作用更少.
- 传统的神经鉴定方法依赖于广泛的数据库,这些数据库可能不完整,导致不准确.
- 机器学习为快速有效的神经预测提供了一个有希望的途径.
研究的目的:
- 开发一种先进的计算工具,用于准确预测神经.
- 克服与传统方法中不完整的前体数据库相关的局限性.
- 为驱动神经预测的特征提供可解释的见解.
主要方法:
- 开发了一种组合方法,结合了四种卷积神经网络 (CNN) 模型.
- 基线CNN模型使用各种编码策略进行训练:一热编码,AAIndex,G-gap二编码和word2vec.
- 这些模型使用高斯天真贝叶斯 (NB) 集成,以创建NeuroCNN_GNB预测器.
- 沙普利增量扩展算法 (SHAP) 算法用于模型的可解释性.
主要成果:
- 与最先进的方法相比,NeuroCNN_GNB在5倍交叉验证和独立测试中表现优越.
- 整体框架有效地利用了不同的数据编码来提高预测准确性.
- SHAP分析成功地确定了对神经预测至关重要的关键特征,有助于理解模型行为.
结论:
- NeuroCNN_GNB代表了计算神经预测的重大进步.
- 框架的可解释性增强了对预测过程的信任和理解.
- 这种方法为神经鉴定传统实验方法提供了强大而有效的替代方案.
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