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基于序列同性得分的深度模糊网络用于识别治疗性

Xiaoyi Guo¹, Ziyu Zheng², Kang Hao Cheong³

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Quzhou People's Hospital, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000, PR China; Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore.

Neural networks : the official journal of the International Neural Network Society

|June 20, 2024

概括

这项研究引入了一种用于检测治疗性的新型计算模型,通过解决噪声来提高准确性. 基于序列同样性得分的深模糊回声状态网络,最大化混合物电流量 (SHS-DFESN-MMC) 在预测方面表现出卓越的性能.

科学领域:

生物医学科学生物医学科学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

治疗性检测至关重要,但传统方法是缓慢的.
计算生物学为检测提供了效率改进.
现有的计算方法往往忽略了噪音,影响了概括.

研究的目的:

开发一种先进的计算模型,用于增强治疗性检测.
为了提高治疗预测的概括性能.
引入一种基于序列同质得分的新型深模糊回声状态网络,并最大化混合物流 (SHS-DFESN-MMC) 模型.

主要方法:

开发了一个SHS-DFESN-MMC模型,包括序列同质性和模糊回声状态网络.
使用最大限度地提高混合物电流度以减轻噪声影响.
在使用十倍交叉验证和独立测试集的八个不同的治疗数据集上验证了模型.

主要成果:

采用SHS-DFESN-MMC模型,在接收器运行特征曲线 (AUC) 值下实现了最高的平均面积.
在所有数据集中,在训练集中达到0.926的平均AUC,在独立测试集中达到0.923.
与治疗性类预测的现有计算方法相比,证明了卓越的性能.

结论:

该SHS-DFESN-MMC模型显著提高治疗检测的准确性和概括性.
这种计算方法为传统的实验方法提供了更有效和更强大的替代方案.
这些发现突出了将深度学习与生物医学应用中的防噪技术相结合的潜力.

关键词:

生物序列分类生物序列分类会员的职能是成为会员.混合物电流变的情况治疗性类的治疗性类.

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