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

VirusWarn: A mutation-based early warning system to prioritize concerning SARS-CoV-2 and influenza virus variants from sequencing data

The rapid evolution of respiratory viruses is characterized by the emergence of variants with concerning phenotypes that are efficient in antibody escape or show high transmissibility. This necessitates timely identification of such variants by surveillance networks to assist public health interventions. Here, we introduce VirusWarn, a comprehensive system designed for detecting, prioritizing, and warning of emerging virus variants from large genomic datasets. VirusWarn uses both manually-curated rules and machine-learning (ML) classifiers to generate and rank pathogen sequences based on mutations of concern and regions of interest. Validation results for SARS-CoV-2 showed that VirusWarn successfully identifies variants of concern in both assessments, with manual- and ML-derived criteria from positive selection analyses. Although initially developed for SARS-CoV-2, VirusWarn was adapted to Influenza viruses and their dynamics, and provides a robust performance, integrating a scheme that accounts for fixed mutations from past seasons. HTML reports provide detailed results with searchable tables and visualizations, including mutation plots and heatmaps. Because VirusWarn is written in Nextflow, it can be easily adapted to other pathogens, demonstrating its flexibility and scalability for genomic surveillance efforts.

GenoM7GNet: An Efficient N7-Methylguanosine Site Prediction Approach Based on a Nucleotide Language Model

N-methylguanosine (m7G), one of the mainstream post-transcriptional RNA modifications, occupies an exceedingly significant place in medical treatments. However, classic approaches for identifying m7G sites are costly both in time and equipment. Meanwhile, the existing machine learning methods extract limited hidden information from RNA sequences, thus making it difficult to improve the accuracy. Therefore, we put forward to a deep learning network, called "GenoM7GNet," for m7G site identification. This model utilizes a Bidirectional Encoder Representation from Transformers (BERT) and is pretrained on nucleotide sequences data to capture hidden patterns from RNA sequences for m7G site prediction. Moreover, through detailed comparative experiments with various deep learning models, we discovered that the one-dimensional convolutional neural network (CNN) exhibits outstanding performance in sequence feature learning and classification. The proposed GenoM7GNet model achieved 0.953in accuracy, 0.932in sensitivity, 0.976in specificity, 0.907in Matthews Correlation Coefficient and 0.984in Area Under the receiver operating characteristic Curve on performance evaluation. Extensive experimental results further prove that our GenoM7GNet model markedly surpasses other state-of-the-art models in predicting m7G sites, exhibiting high computing performance.

A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs and diseases within a graph. However, labeled data for drug indication prediction is often limited and expensive to acquire. Contrastive learning addresses this challenge by aligning similar drug-disease pairs and separating dissimilar pairs in the embedding space. Thus, we developed a model called DrIGCL for drug indication prediction, which utilizes graph convolutional networks and contrastive learning. DrIGCL incorporates drug structure, disease comorbidities, and known drug indications to extract representations of drugs and diseases. By combining contrastive and classification losses, DrIGCL predicts drug indications effectively. In multiple runs of hold-out validation experiments, DrIGCL consistently outperformed existing computational methods for drug indication prediction, particularly in terms of top-k. Furthermore, our ablation study has demonstrated a significant improvement in the predictive capabilities of our model when utilizing contrastive learning. Finally, we validated the practical usefulness of DrIGCL by examining the predicted novel indications of Aspirin.

Strengthening deep-learning models for intracranial hemorrhage detection: strongly annotated computed tomography images and model ensembles

Background and purpose

Multiple attempts at intracranial hemorrhage (ICH) detection using deep-learning techniques have been plagued by clinical failures. We aimed to compare the performance of a deep-learning algorithm for ICH detection trained on strongly and weakly annotated datasets, and to assess whether a weighted ensemble model that integrates separate models trained using datasets with different ICH improves performance.

Methods

We used brain CT scans from the Radiological Society of North America (27,861 CT scans, 3,528 ICHs) and AI-Hub (53,045 CT scans, 7,013 ICHs) for training. DenseNet121, InceptionResNetV2, MobileNetV2, and VGG19 were trained on strongly and weakly annotated datasets and compared using independent external test datasets. We then developed a weighted ensemble model combining separate models trained on all ICH, subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), and small-lesion ICH cases. The final weighted ensemble model was compared to four well-known deep-learning models. After external testing, six neurologists reviewed 91 ICH cases difficult for AI and humans.

Results

InceptionResNetV2, MobileNetV2, and VGG19 models outperformed when trained on strongly annotated datasets. A weighted ensemble model combining models trained on SDH, SAH, and small-lesion ICH had a higher AUC, compared with a model trained on all ICH cases only. This model outperformed four deep-learning models (AUC [95% C.I.]: Ensemble model, 0.953[0.938-0.965]; InceptionResNetV2, 0.852[0.828-0.873]; DenseNet121, 0.875[0.852-0.895]; VGG19, 0.796[0.770-0.821]; MobileNetV2, 0.650[0.620-0.680]; p < 0.0001). In addition, the case review showed that a better understanding and management of difficult cases may facilitate clinical use of ICH detection algorithms.

Conclusion

We propose a weighted ensemble model for ICH detection, trained on large-scale, strongly annotated CT scans, as no model can capture all aspects of complex tasks.

HostNet: improved sequence representation in deep neural networks for virus-host prediction

BACKGROUND

The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand.

RESULTS

To overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of "Rabies lyssavirus" and an in-house dataset of "Flavivirus". Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability.

CONCLUSION

HostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.

CLCAP: Contrastive learning improves antigenicity prediction for influenza A virus using convolutional neural networks

Influenza viruses are detected year-round over the world and the viruses will usually circulate during fall and winter, causing the seasonal flu. The growing novel variants of influenza viruses pose a significant concern to public health annually. However, the rapid mutation of the influenza viruses makes it challenging to timely track their evolution. Therefore, a fast, low-cost, and precise method to predict the antigenic variant of influenza viruses could help vaccine development and prevent viral transmission. In this study, we propose a multi-channel convolutional neural network using contrastive learning to predict the antigenicity of influenza A viruses. An integrated dataset containing antigenic data and protein sequences was collected from various public resources and literature. The experimental results on three different influenza subtypes indicate our proposed model outperforms other traditional machine learning classifiers for antigenicity prediction. In addition, it also demonstrates superior performance over several state-of-the-art approaches, with 5.18 %, 7.03 % and 7.82 % increase in accuracy compared to the best results for H1N1, H3N2 and H5N1, respectively. The proposed framework is timely and effective in influenza antigenicity prediction and can be adapted to the study of other viruses.

IAV-CNN: A 2D Convolutional Neural Network Model to Predict Antigenic Variants of Influenza A Virus

The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we apply a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combining the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.

Organizing the bacterial annotation space with amino acid sequence embeddings

BACKGROUND

Due to the ever-expanding gap between the number of proteins being discovered and their functional characterization, protein function inference remains a fundamental challenge in computational biology. Currently, known protein annotations are organized in human-curated ontologies, however, all possible protein functions may not be organized accurately. Meanwhile, recent advancements in natural language processing and machine learning have developed models which embed amino acid sequences as vectors in n-dimensional space. So far, these embeddings have primarily been used to classify protein sequences using manually constructed protein classification schemes.

RESULTS

In this work, we describe the use of amino acid sequence embeddings as a systematic framework for studying protein ontologies. Using a sequence embedding, we show that the bacterial carbohydrate metabolism class within the SEED annotation system contains 48 clusters of embedded sequences despite this class containing 29 functional labels. Furthermore, by embedding Bacillus amino acid sequences with unknown functions, we show that these unknown sequences form clusters that are likely to have similar biological roles.

CONCLUSIONS

This study demonstrates that amino acid sequence embeddings may be a powerful tool for developing more robust ontologies for annotating protein sequence data. In addition, embeddings may be beneficial for clustering protein sequences with unknown functions and selecting optimal candidate proteins to characterize experimentally.

A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics.

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

Cancer still ranks as a leading cause of mortality worldwide. Although considerable efforts have been dedicated to anticancer therapeutics, progress is still slow, partially due to the absence of robust prediction models. Multicellular tumor spheroids, as a major three-dimensional (3D) culture model exhibiting features of avascular tumors, gained great popularity in pathophysiological studies and high throughput drug screening. However, limited control over cellular and structural organization is still the key challenge in achieving in vivo like tissue microenvironment. 3D bioprinting has made great strides toward tissue/organ mimicry, due to its outstanding spatial control through combining both cells and materials, scalability, and reproducibility. Prospectively, harnessing the power from both 3D bioprinting and multicellular spheroids would likely generate more faithful tumor models and advance our understanding on the mechanism of tumor progression. In this review, the emerging concept on using spheroids as a building block in 3D bioprinting for tumor modeling is illustrated. We begin by describing the context of the tumor microenvironment, followed by an introduction of various methodologies for tumor spheroid formation, with their specific merits and drawbacks. Thereafter, we present an overview of existing 3D printed tumor models using spheroids as a focus. We provide a compilation of the contemporary literature sources and summarize the overall advancements in technology and possibilities of using spheroids as building blocks in 3D printed tissue modeling, with a particular emphasis on tumor models. Future outlooks about the wonderous advancements of integrated 3D spheroidal printing conclude this review.