Biology subjects

Nguyen, H.-H.

Publications and source records attributed to Nguyen, H.-H..

3 recordsLinked to original sources

bioRxiv2026

Interpreting the WaveSeekerNet model to reveal the evolution and biology of influenza A virus

BackgroundInfluenza A virus (IAV) is a major public health burden, causing seasonal epidemics and occasional pandemics. Its transmission from avian species to mammals and subsequent spread requires adaptive changes in the viral genome. Understanding these molecular adaptations is essential for pandemic preparedness, and machine learning offers a powerful approach to uncover the evolution and biology of IAV. ResultsThis study established a well-calibrated WaveSeekerNet model that accurately predicted the host source across all 8 IAV segments (macro F1-score: 0.9728), significantly improving the reliability of predicted probabilities with calibration errors approaching zero. Model interpretation revealed that avian-adapted IAVs consistently activated G/C content, whereas mammalian-adapted IAVs generally activated A/T content. This distinction was confirmed by codon-level analysis, in which G/C-rich codons were rewarded for the avian hosts and A/T-rich codons for the mammalian hosts. In the feature space learned by WaveSeekerNet, we defined host-adaptive distance to quantify species barriers and proposed it as a risk-assessment metric. We hypothesized the Mammalian Adaptation Zone (MAZ), a zone where the virus is expected to adjust its host-adaptive distance to reach, thereby helping it establish persistent mammalian lineages. The analysis also revealed the Hard Distance of avian-origin viruses (e.g., H5Nx, H9N2), indicating they have not yet established persistent mammalian lineages. Finally, analysis of human H7N9 (2013, China) and non-human mammalian H5Nx (North America) viruses showed that WaveSeekerNet accurately identified key mammalian-adaptive mutations, including PB2-E627K and PB2-D701N. ConclusionsWaveSeekerNet elucidated IAV host-adaptation mechanisms in silico, providing insights into the underlying mechanisms of host adaptation and informing improved surveillance and intervention strategies.

genomics↗

bioRxiv2025

WaveSeekerNet: Accurate Prediction of Influenza A Virus Subtypes and Host Source Using Attention-Based Deep Learning

BackgroundInfluenza A virus (IAV) poses a significant threat to animal health globally, with its ability to overcome species barriers and cause pandemics. Rapid and accurate IAV subtypes and host source prediction is crucial for effective surveillance and pandemic preparedness. Deep learning has emerged as a powerful tool for analyzing viral genomic sequences, offering new ways to uncover hidden patterns associated with viral characteristics and host adaptation. FindingsWe introduce WaveSeekerNet, a novel deep learning model for accurate and rapid prediction of IAV subtypes and host source. The model leverages attention-based mechanisms and efficient token mixing schemes, including the Fourier Transform and the Wavelet Transform, to capture intricate patterns within viral RNA and protein sequences. Extensive experiments on diverse datasets demonstrate WaveSeekerNets superior performance to existing models that use the traditional self-attention mechanism. Notably, WaveSeekerNet rivals VADR (Viral Annotation DefineR) in subtype prediction using the high-quality RNA sequences, achieving the maximum score of 1.0 on metrics including the Balanced Accuracy, F1-score (Macro Average), and Matthews Correlation Coefficient (MCC). Our approach to subtype and host source prediction also exceeds the pre-trained ESM-2 (Evolutionary Scale Modeling) models with respect to generalization performance and computational cost. Furthermore, WaveSeekerNet exhibits remarkable accuracy in distinguishing between human, avian, and other mammalian hosts. The ability of WaveSeekerNet to flag potential cross-species transmission events underscores its significant value for real-time surveillance and proactive pandemic preparedness efforts. ConclusionsWaveSeekerNets superior performance, efficiency, and ability to flag potential cross-species transmission events highlight its potential for real-time surveillance and pandemic preparedness. This model represents a significant advancement in applying deep learning for IAV classification and holds promise for future epidemiological, veterinary studies, and public health interventions.

bioinformatics↗

bioRxiv2025

An Experimental and Multiphysics Simulations Study of Clostridium carboxidivorans sp. 624 for Acid and Alcohol Production in CO2/H2

Biotechnological advances in CO2 utilization through gas fermentation offer a sustainable alternative to energy-intensive chemical processes. This study investigates and optimizes fermentation dynamics of Clostridium carboxidivorans sp. 624 for enhanced production of C2-C6 acids via combined experimental and computational methods. A systematic experimental evaluation of temperature and medium conditions, along with time-course analysis elucidates metabolic pathway regulation. Experimental optimisation, complemented by modelling, identified gas-liquid volumetric ratios VL/VG = 4 as optimal for maximizing longer-chain acids production, attributed to enhanced gas solubility and substrate bioavailability. Additionally, a hybrid model combining dynamic-kinetic model and computational fluid dynamics (CFD) successfully predict product formation. By capturing spatial inhomogeneities in gas-liquid interactions, the model also provides critical insights for optimizing fermentation performance and establishes a framework for improving process design parameters for CO2/H2 fermentation, paving the way for scalable and efficient CO2-based bioprocesses.

microbiology↗