ウイルスゲノムの特徴量解析と自然宿主推定への応用

我继续研究机器学习，包括服务器维护和优化，学习和使用不同的ML库，参加会议，并探索最新的论文，教程和行业标准。两篇合著论文：- Tessmer HL，Ito K和Omori R。机器能学习呼吸道病毒流行病学吗？流行病学动态估计的无似然方法的比较研究。Sakon N，Komano J，Tessmer HL和Omori R.在2016/17季节期间，日本大坂的婴儿和学龄儿童中诺如病毒的高传播性摘要：为了估计和预测呼吸道病毒的传播动态，基本繁殖数R 0的估计是必不可少的。最近，近似贝叶斯计算方法已被用作估计流行病学模型参数，特别是R 0的似然自由方法。在本文中，我们探索了各种机器学习方法，多层感知器，卷积神经网络和长短期记忆，学习和估计参数。此外，我们还比较了机器学习和近似贝叶斯计算方法对甲型H1N1流感pdm 09、腮腺炎和麻疹爆发的模拟和真实流行病学数据的估计和时间要求的准确性。我们发现，机器学习方法可以比近似贝叶斯计算方法更快地进行验证和测试，但近似贝叶斯计算方法在不同的数据集上更具鲁棒性。

I continued my research into machine learning, including server maintenance and optimization, learning and using different ML libraries, attending conferences, and exploring the latest papers, tutorials, and industry standards.Two co-authored papers:- Tessmer HL, Ito K, and Omori R. Can machines learn respiratory virus epidemiology?: A comparative study of likelihood-free methods for the estimation of epidemiological dynamics.- Sakon N, Komano J, Tessmer HL, and Omori R. High transmissibility of norovirus among infants and school children during the 2016/17 season in Osaka, Japan.Abstract: To estimate and predict the transmission dynamics of respiratory viruses, the estimation of the basic reproduction number, R0, is essential. Recently, approximate Bayesian computation methods have been used as likelihood free methods to estimate epidemiological model parameters, particularly R0. In this paper, we explore various machine learning approaches, the multi-layer perceptron, convolutional neural network, and long-short term memory, to learn and estimate the parameters. Further, we compare the accuracy of the estimates and time requirements for machine learning and the approximate Bayesian computation methods on both simulated and real-world epidemiological data from outbreaks of influenza A(H1N1)pdm09, mumps, and measles. We find that the machine learning approaches can be verified and tested faster than the approximate Bayesian computation method, but that the approximate Bayesian computation method is more robust across different datasets.