VirPreNet: a weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all eight segments 

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. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics. Results In this paper, we propose a weighted ensemble convolutional neural network for the virulence prediction of influenza A viruses named VirPreNet that uses all 8 segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the 8-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble convolutional neural network is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNet’s main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza virulence. Availability and Implementation Codes and data to generate the VirPreNet are publicly available at https://github.com/Rayin-saber/VirPreNet

ViPal: A Framework for Virulence Prediction of Influenza Viruses with Prior Viral Knowledge Using Genomic Sequences

“…The model PART achieved the best performance. The other approach VirPreNet [62] developed a weighted ensemble convolutional neural network for virulence prediction of influenza A viruses based on all eight segments. Figure 4 presented predictive performance and computation cost between ViPal and the existing models.…”

Using Spheroids as Building Blocks Towards 3D Bioprinting of Tumor Microenvironment

“…integrated a machine learning model using least general generalization algorithm combined with yield stress, viscoelasticity, and shape fidelity from using various type I collagen-based bio-inks[ 190 ]. By separating the class variables into shape fidelity and extrusion, the machine learning algorithm effectively optimized the composite bio-ink material fraction and subsequent printing performance[ 191 , 192 ]. Current applications of 3D bioprinting based machine learning algorithms are currently geared towards using regressive models such as LASSO; however, a potential avenue of integrating advanced learning systems using generative ensembles or Bayesian approaches in producing highest performing inks of spheroidal assembly remains completely untapped.…”

Editorial: Computational methods to analyze RNA data for human diseases

“…Although multiple databases offer information on virus-host protein interactions, they often lack detailed information about strain-specific virulence factors or the specific protein domains implicated in the interactions ( Yin et al, 2017 ; Yin et al, 2021 ). Several databases may have incomplete representation coverage of influenza strains of influenza strains due to the challenge of sifting through extensive literature to gather comprehensive information.…”