The Influenza type A virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Haemagglutinin (HA) gene of the virus has the potential to be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. In this paper, to accurately predict if an Influenza type A virus has the capability to infect human hosts, by using only the HA gene, is therefore developed and tested. The predictive model follows three main steps; (i) decoding the protein sequences into numerical signals using EIIP amino acid scale, (ii) analysing these sequences by using Discrete Fourier Transform (DFT) and extracting DFT-based features, (iii) using a predictive model, based on Artificial Neural Networks and using the features generated by DFT. In this analysis, from the Influenza Research Database, 30724, 18236 and 8157 HA protein sequences were collected for Human, Avian and Swine, respectively. Given this set of the proteins, the proposed method yielded 97.36% (+- 0.04%), 97.26% (+- 0.26%), 0.978 (+- 0.004), 0.963 (+- 0.005) and 0.945 (+- 0.005) for the training accuracy validation accuracy, precision, recall and Mathews Correlation Coefficient (MCC) respectively, based on a 10-fold cross-validation. The classification model generated by using one of the largest dataset, if not the largest, yields promising results that could lead to early detection of such species and help develop precautionary measurements for possible human infections.
Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks
Computation-based Science and Technology Research Center, The Cyprus Institute（塞浦路斯研究所计算科学与技术研究中心）；Faculty of Computing, Engineering and the Built Environment, Birmingham City University（伯明翰城市大学计算、工程与建筑环境学院）
Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase
University of Leeds（利兹大学）；Toronto Metropolitan University（多伦多都会大学）；RIKEN iTHEMS（理化学研究所创新人工智能与多学科系统中心）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Quantifying the relative contribution of free virus and cell-to-cell transmission routes to the propagation of hepatitis C virus infections in vitro using an agent-based model
Semi-Quantitative Analysis and Seroepidemiological Evidence of Past Dengue Virus Infection among HIV-infected patients in Onitsha, Anambra State, Nigeria
Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning
The Pennsylvania State University（宾夕法尼亚州立大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；George Washington University（乔治华盛顿大学）；National Institute of Allergy and Infectious Diseases（美国国家过敏和传染病研究所）；National Institutes of Health（美国国立卫生研究院）
Does the random nature of cell-virus interactions during in vitro infections affect TCID$_{50}$ measurements and parameter estimation by mathematical models?
