结合Transformer与Kolmogorov Arnold网络的分子扩增时序预测研究  

Molecular amplification time series prediction research combining Transformer with Kolmogorov-Arnold network

作　　者：刘灿锋 孙浩 东辉 LIU Canfeng;SUN Hao;DONG Hui(School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou Fujian 350108,China;School of Mechatronics Engineering,Harbin Institute of Technology,Harbin Heilongjiang 150001,China)

机构地区：[1]福州大学机械工程及自动化学院,福建福州350108 [2]哈尔滨工业大学机电工程学院,黑龙江哈尔滨150001

出　　处：《图学学报》2024年第6期1256-1265,共10页Journal of Graphics

基　　金：国家自然科学基金(62173093,61604042);福建省杰出青年自然科学基金(2023J01310195)。

摘　　要：随着医辽诊断和治疗干预技术的不断进步,医学时间序列数据呈现指数级增长。人工智能(AI),尤其是深度学习在挖掘医学时间序列数据潜在信息方面展现出巨大潜力。为此,首次提出将Transformer与Kolmogorov arnold网络(KAN)相结合的方法,用于核酸扩增实验数据的预测分析。通过实验数据分析,证实模型在准确预测扩增趋势和终点值方面的有效性,终点值误差仅为1.87,R-square系数为0.98,且模型能准确识别不同样本类型的实验数据。进一步地,通过消融实验和超参数调优,深入探究模型各组成部分及其参数对预测性能的影响。最后,在911条临床数据上对10种深度学习模型进行泛化能力测试的结果表明,Transformer-KAN模型在预测准确性和泛化能力上均优于其他模型,不仅为改进大流行病常规诊断技术提供了新视角,还为进一步研究KAN模型及相应基础理论提供了实验佐证。With the development of medical diagnosis and treatment intervention techniques,there has been an exponential growth in medical data along time series.Artificial intelligence(AI),particularly deep learning(DL),has demonstrated significant potential in uncovering medical data along time series.This study proposed,for the first time,a method that integrates the Transformer architecture with the Kolmogorov-Arnold network(KAN)to enable predictive analysis of nucleic acid amplification experimental data.Through experimental data analysis methods,the effectiveness of the model in accurately predicting amplification trends and endpoint values was validated,achieving an endpoint value error of merely 1.87 and an R-square coefficient as high as 0.98.Moreover,the model was capable of effectively identifying experimental data from different sample types.Furthermore,this research delved into the impact of the model’s components and parameters on predictive performance through ablation experiments and hyperparameter tuning.Finally,a generalization capability test was conducted on 911 clinical data records provided by the Fujian Provincial Hospital across 10 deep learning models.The results demonstrated that the proposed Transformer-KAN network outperformed other models in terms of predictive accuracy and generalization capability.This study not only provided a new perspective for improving routine diagnostic techniques during pandemics but also offered empirical evidence for further research on the KAN model and its corresponding foundational theories.

关 键 词：深度学习 时间序列预测 核酸扩增检测技术 Kolmogorov-Arnold网络 TRANSFORMER 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术] R-05[医药卫生]