COVID-19

Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.

BACKGROUND: Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-consuming and labor-intensive. Several computational methods have been developed to predict antibody-antigen interaction while suffering from the lack ...

KnowVID-19: A Knowledge-Based System to Extract Targeted COVID-19 Information from Online Medical Repositories.

We present KnowVID-19, a knowledge-based system that assists medical researchers and scientists in extracting targeted information quickly and efficiently from online medical literature repositories, such as PubMed, PubMed Central, and other biomedical sources. The system utilizes various open-source machine learning tools, such as GROBID, S2ORC, and BioC to streamline the processes of data extrac...

Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time.

With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening technique...

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the...

Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images.

The early and highly accurate prediction of COVID-19 based on medical images can speed up the diagnostic process and thereby mitigate disease spread; ...

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases.

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics ...

Co-Mask R-CNN: collaborative learning-based method for tooth instance segmentation.

Traditional tooth image analysis methods primarily focus on feature extraction from individual images, often overlooking critical tooth shape and posi...

Detection of carcinoembryonic antigen specificity using microwave biosensor with machine learning.

Early diagnosis and screening of tumor markers are essential for effective cancer treatment and improve the treatment efficiency and prognosis of tumo...

Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning.

INTRODUCTION: The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed...

Construction of the miRNA/Pyroptosis-Related Molecular Regulatory Axis in Abdominal Aortic Aneurysm: Evidence From Transcriptome Data Combined With Multiple Machine Learning Approaches Followed by Experiment Validation.

Abdominal aortic aneurysm (AAA) represents a permanent and localized widening of the abdominal aorta, posing a potentially lethal risk of aortic rupt...

GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.

At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study patholo...

ANTIPASTI: Interpretable prediction of antibody binding affinity exploiting normal modes and deep learning.

The high binding affinity of antibodies toward their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodi...

Graph Curvature Flow-Based Masked Attention.

Graph neural networks (GNNs) have revolutionized drug discovery in chemistry and biology, enhancing efficiency and reducing resource demands. However,...

Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches.

Lung cancer (LC) is a significant global health issue, with smoking as the most common cause. Recent epidemiological studies have suggested that indiv...

Artificial intelligence can be trained to predict -11 mutational status of canine mast cell tumors from hematoxylin and eosin-stained histological slides.

Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical beh...

Clinically Guided Adaptive Machine Learning Update Strategies for Predicting Severe COVID-19 Outcomes.

BACKGROUND: Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic n...

TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity ...

USCT-UNet: Rethinking the Semantic Gap in U-Net Network From U-Shaped Skip Connections With Multichannel Fusion Transformer.

Medical image segmentation is a crucial component of computer-aided clinical diagnosis, with state-of-the-art models often being variants of U-Net. De...

Deep convolutional neural network for automatic segmentation and classification of jaw tumors in contrast-enhanced computed tomography images.

The purpose of this study was to evaluate the performance of convolutional neural network (CNN)-based image segmentation models for segmentation and c...

Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning.

Atherosclerosis (AS) is a predominant etiological factor in numerous cardiovascular diseases, with its associated complications such as myocardial inf...