Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks

2020 ◽  
Vol 18 (01) ◽  
pp. 2040002 ◽  
Author(s):  
Rui Yin ◽  
Yu Zhang ◽  
Xinrui Zhou ◽  
Chee Keong Kwoh
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Influenza A ◽  
Viral Evolution ◽  
Computational Prediction ◽  
Single Step ◽  
Influenza Viruses ◽  
Computational Method ◽  
Influenza Vaccines

Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection. By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the recommendation of the influenza vaccines using recurrent neural networks (RNNs). The Encoder-decoder architecture of RNN model enables us to perform sequence-to-sequence prediction. We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and recommended vaccines is greater than 98% and the [Formula: see text] indicates their antigenic similarity. The multi-step vaccine prediction further demonstrates the robustness of our method which achieves comparable results in contrast to single step prediction. The results show significant matches of the recommended vaccine strains to the circulating strains. We believe it would facilitate the process of vaccine selection and surveillance of seasonal influenza epidemics.

Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks

2020 ◽  
Vol 36 (9) ◽  
pp. 2697-2704 ◽  
Author(s):  
Rui Yin ◽  
Emil Luusua ◽  
Jan Dabrowski ◽  
Yu Zhang ◽  
Chee Keong Kwoh
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Influenza A ◽  
Sequence Data ◽  
Predictive Performance ◽  
Influenza Viruses ◽  
Supplementary Information ◽  
Influenza A Viruses ◽  
Mutation Prediction

Abstract Motivation Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. The goal of this work is to predict whether mutations are likely to occur in the next flu season using historical glycoprotein hemagglutinin sequence data. One of the major challenges is to model the temporality and dimensionality of sequential influenza strains and to interpret the prediction results. Results In this article, we propose an efficient and robust time-series mutation prediction model (Tempel) for the mutation prediction of influenza A viruses. We first construct the sequential training samples with splittings and embeddings. By employing recurrent neural networks with attention mechanisms, Tempel is capable of considering the historical residue information. Attention mechanisms are being increasingly used to improve the performance of mutation prediction by selectively focusing on the parts of the residues. A framework is established based on Tempel that enables us to predict the mutations at any specific residue site. Experimental results on three influenza datasets show that Tempel can significantly enhance the predictive performance compared with widely used approaches and provide novel insights into the dynamics of viral mutation and evolution. Availability and implementation The datasets, source code and supplementary documents are available at: https://drive.google.com/drive/folders/15WULR5__6k47iRotRPl3H7ghi3RpeNXH. Supplementary information Supplementary data are available at Bioinformatics online.

A review of irregular time series data handling with gated recurrent neural networks

2021 ◽  
Vol 441 ◽  
pp. 161-178
Author(s):  
Philip B. Weerakody ◽  
Kok Wai Wong ◽  
Guanjin Wang ◽  
Wendell Ela
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Series Data ◽  
Data Handling

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

DYNAMICAL RECURRENT NEURAL NETWORKS — TOWARDS ENVIRONMENTAL TIME SERIES PREDICTION

1995 ◽  
Vol 06 (02) ◽  
pp. 145-170 ◽  
Author(s):  
ALEX AUSSEM ◽  
FIONN MURTAGH ◽  
MARC SARAZIN
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Time Series Prediction ◽  
Predictive Ability ◽  
Complex Problem ◽  
Internal Variables ◽  
Internal Dynamic ◽  
K Nearest Neighbors ◽  
Fuzzy Coding

Dynamical Recurrent Neural Networks (DRNN) (Aussem 1995a) are a class of fully recurrent networks obtained by modeling synapses as autoregressive filters. By virtue of their internal dynamic, these networks approximate the underlying law governing the time series by a system of nonlinear difference equations of internal variables. They therefore provide history-sensitive forecasts without having to be explicitly fed with external memory. The model is trained by a local and recursive error propagation algorithm called temporal-recurrent-backpropagation. The efficiency of the procedure benefits from the exponential decay of the gradient terms backpropagated through the adjoint network. We assess the predictive ability of the DRNN model with meteorological and astronomical time series recorded around the candidate observation sites for the future VLT telescope. The hope is that reliable environmental forecasts provided with the model will allow the modern telescopes to be preset, a few hours in advance, in the most suited instrumental mode. In this perspective, the model is first appraised on precipitation measurements with traditional nonlinear AR and ARMA techniques using feedforward networks. Then we tackle a complex problem, namely the prediction of astronomical seeing, known to be a very erratic time series. A fuzzy coding approach is used to reduce the complexity of the underlying laws governing the seeing. Then, a fuzzy correspondence analysis is carried out to explore the internal relationships in the data. Based on a carefully selected set of meteorological variables at the same time-point, a nonlinear multiple regression, termed nowcasting (Murtagh et al. 1993, 1995), is carried out on the fuzzily coded seeing records. The DRNN is shown to outperform the fuzzy k-nearest neighbors method.

Using Genetic Programming to Extract Knowledge from Artificial Neural Networks

2008 ◽  
pp. 308-327
Author(s):  
Daniel Rivero ◽  
Miguel Varela ◽  
Javier Pereira
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Recurrent Neural Networks ◽  
Know How ◽  
Artificial Neural

A technique is described in this chapter that makes it possible to extract the knowledge held by previously trained artificial neural networks. This makes it possible for them to be used in a number of areas (such as medicine) where it is necessary to know how they work, as well as having a network that functions. This chapter explains how to carry out this process to extract knowledge, defined as rules. Special emphasis is placed on extracting knowledge from recurrent neural networks, in particular when applied in predicting time series.

scholarly journals Generation and Characterization of Universal Live-Attenuated Influenza Vaccine Candidates Containing Multiple M2e Epitopes

Vaccines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 648
Author(s):  
Tatiana Kotomina ◽  
Irina Isakova-Sivak ◽  
Ki-Hye Kim ◽  
Bo Ryoung Park ◽  
Yu-Jin Jung ◽  
...  
Keyword(s):  
Specific Antibody ◽  
Influenza A ◽  
Tandem Repeats ◽  
Survival Rates ◽  
Protective Role ◽  
Influenza Viruses ◽  
Influenza Vaccines ◽  
Cell Mediated Immunity ◽  
Universal Vaccine ◽  
Specific Antibodies

Influenza viruses constantly evolve, reducing the overall protective effect of routine vaccination campaigns. Many different strategies are being explored to design universal influenza vaccines capable of protecting against evolutionary diverged viruses. The ectodomain of influenza A M2e protein (M2e) is among the most promising targets for universal vaccine design. Here, we generated two recombinant live attenuated influenza vaccines (LAIVs) expressing additional four M2e tandem repeats (4M2e) from the N-terminus of the viral hemagglutinin (HA) protein, in an attempt to enhance the M2e-mediated cross-protection. The recombinant H1N1+4M2e and H3N2+4M2e viruses retained growth characteristics attributable to traditional LAIV viruses and induced robust influenza-specific antibody responses in BALB/c mice, although M2e-specific antibodies were raised only after two-dose vaccination with LAIV+4M2e viruses. Mice immunized with either LAIV or LAIV+4M2e viruses were fully protected against a panel of heterologous influenza challenge viruses suggesting that antibody and cell-mediated immunity contributed to the protection. The protective role of the M2e-specific antibody was seen in passive serum transfer experiments, where enhancement in the survival rates between classical LAIV and chimeric H3N2+4M2e LAIV was demonstrated for H3N2 and H5N1 heterologous challenge viruses. Overall, the results of our study suggest that M2e-specific antibodies induced by recombinant LAIV+4M2e in addition to cellular immunity by LAIV play an important role in conferring protection against heterologous viruses.

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