Influenza A virus poses a significant threat to global health due to its high mutability and potential for pandemics. Accurate prediction of influenza virus pandemic risk can mitigate their threat to public health systems and societal stability. Current sequence-based deep learning methods predict the risk of viral pandemics by extracting features from the viral genome sequence. However, these methods ignore the gene flow between different virus strains through genetic reassortment, which is essential for understanding the dynamics of viral evolution. To leverage the gene flow of virus strains to predict the pandemic, we propose Graph Cross Attention Networks for influenza pandemic identification (GCAN-Flu). GCAN-Flu begins with constructing an influenza virus gene flow network based on the genetic reassortment relationship between viruses, and then encodes the gene flow information between strains as a latent modal representation. We further use Graph Cross Attention (GCA) layers to integrate the viral genome sequence with the gene flow information, and predict pandemic risks based on the multimodal representations. Experimental results demonstrate that GCAN-Flu outperforms current methods, providing a powerful tool for pandemic forecasting.

Cell-penetrating peptides (CPPs) have been shown to be a transport vehicle for delivering cargoes into live cells, offering great potential as future therapeutics. It is essential to identify CPPs for better understanding of their functional mechanisms. Machine learning-based methods have recently emerged as a main approach for computational identification of CPPs. However, one of the main challenges and difficulties is to propose an effective feature representation model that sufficiently exploits the inner difference and relevance between CPPs and non-CPPs, in order to improve the predictive performance. In this paper, we have developed CPPred-FL, a powerful bioinformatics tool for fast, accurate and large-scale identification of CPPs. In our predictor, we introduce a new feature representation learning scheme that enables one to learn feature representations from totally 45 well-trained random forest models with multiple feature descriptors from different perspectives, such as compositional information, position-specific information and physicochemical properties, etc. We integrate class and probabilistic information into our feature representations. To improve the feature representation ability, we further remove redundant and irrelevant features by feature space optimization. Benchmarking experiments showed that CPPred-FL, using 19 informative features only, is able to achieve better performance than the state-of-the-art predictors. We anticipate that CPPred-FL will be a powerful tool for large-scale identification of CPPs, facilitating the characterization of their functional mechanisms and accelerating their applications in clinical therapy.

BACKGROUND: Avian influenza virus can directly cross species barriers and infect humans with high fatality. As antigen novelty for human host, the public health is being challenged seriously. The pandemic risk of avian influenza viruses should be analyzed and a prediction model should be constructed for virology applications. RESULTS: The 178 signature positions in 11 viral proteins were firstly screened as features by the scores of five amino acid factors and their random forest rankings. The Supporting Vector Machine algorithm achieved well performance. The most important amino acid factor (Factor 5) and the minimal range of signature positions (63 amino acid residues) were also explored. Moreover, human-origin avian influenza viruses with three or four genome segments from human virus had pandemic risk with high probability. CONCLUSION: Using machine learning methods, the present paper scores the amino acid mutations and predicts pandemic risk with well performance. Although long evolution distances between avian and human viruses suggest that avian influenza virus in nature still need time to fix among human host, it should be notable that there are high pandemic risks for H7N9 and H9N2 avian viruses.