Latest AI and machine learning research in gerd for healthcare professionals.
Accurately annotating the functions of uncharacterised human proteins remains a major bottleneck in biology. We present MkAtt–SDN2GO, a neural architecture that extends SDN2GO by integrating adaptive multi-kernel convolution and attention mechanisms to predict Gene Ontology terms from protein sequences, domains, and protein–protein interaction (PPI) context. The sequence stream employs a learnable...
Proteins are essential in biological processes, primarily through their interactions with other molecules, including proteins. These interactions are crucial for cellular functions and maintaining life. Predicting Protein-Protein Interactions (PPIs) is very important, although challenging, for understanding cellular functions and diseases. This paper presents BioPrediction-PPI, a new end-to-end Ma...
Protein-protein interactions (PPIs) are essential for the study of cellular function, yet computational prediction of bacterial PPIs remains limited. ...
Robust prediction of protein–protein interactions (PPIs) requires models that generalize beyond the training distribution. Here, we present PLMDA-PPI,...
With the growing pervasiveness of pre-trained protein large language models (pLLMs), pLLM-based methods are increasingly being put forward for the pro...
Computational models of stress responses can highlight candidate genes underlying physiological adaptation, but their utility depends on rigorous vali...
Individuals homozygous for the Alpha-1 Antitrypsin (AAT) Z allele (Pi*ZZ) exhibit heterogeneity in COPD risk. COPD occurrence in non-smokers with AAT ...
Several conditions exist that do not have their own unique diagnosis code in widely-used clinical terminologies, making them difficult to track and st...
Self-attention mechanisms and token embeddings behind transformers allow the extraction of complex patterns from large datasets, and enhance the predi...
Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitation...
Accurate evaluation of aortic regurgitation (AR) severity is necessary for early detection and chronic disease management. AR is most commonly assesse...
Gastric cancer (GC) is the fourth most common cause of cancer death worldwide, with a 5-year survival rate of less than 40%. One of the most important...
Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient’s health and quality of life. Conventional normal t...
Artificial intelligence (AI) has emerged as a transformative force in gastrointestinal (GI) endoscopy, offering enhancements in diagnostic accuracy, l...
Prediction-powered inference (PPI) [1] and its subsequent development called PPI++ [2] provide a novel approach to standard statistical estimation, le...
Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific anatomical and functional cardiac abnormalities. ...
Machine learning models for predicting structural heart disease (SHD) from electrocardiography (ECG) traditionally required structured echocardiograph...
Fluid biomarkers are emerging as crucial markers for diagnosis and disease monitoring in neurology. Epilepsy remains an exception despite seizures bei...
Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor inter-and intra-ra...
Accurate medical image classification is critical for early diagnosis and effective treatment planning. However, conventional deep learning models oft...