Peptic Ulcer Disease

MkAtt-SDN2GO: Multi-kernel Attentive-SDN2GO Network for Protein Function Prediction in Humans

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...

BioPrediction-PPI: Simplifying the Prediction of Protein-Protein actions through Artificial Intelligence

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...

B-PPI: A Cross-Attention Model for Large-Scale Bacterial Protein-Protein Interaction Prediction

Protein-protein interactions (PPIs) are essential for the study of cellular function, yet computational prediction of bacterial PPIs remains limited. ...

Mechanism-Aware Inductive Bias Enhances Generalization in Protein-Protein Interaction Prediction

Robust prediction of protein–protein interactions (PPIs) requires models that generalize beyond the training distribution. Here, we present PLMDA-PPI,...

A flaw in using pre-trained pLLMs in protein-protein interaction inference models

With the growing pervasiveness of pre-trained protein large language models (pLLMs), pLLM-based methods are increasingly being put forward for the pro...

A Context-Specific, Literature-Supported Framework for Validating Stress Response Models in Mammals

Computational models of stress responses can highlight candidate genes underlying physiological adaptation, but their utility depends on rigorous vali...

Assessing Inflammatory Protein Biomarkers in COPD Subjects with and without Alpha-1 Antitrypsin Deficiency

Individuals homozygous for the Alpha-1 Antitrypsin (AAT) Z allele (Pi*ZZ) exhibit heterogeneity in COPD risk. COPD occurrence in non-smokers with AAT ...

ICU Readmission Prediction for Intracerebral Hemorrhage Patients using MIMIC III and MIMIC IV Databases

Intracerebral hemorrhage (ICH) is a critical form of stroke resulting from bleeding within the brain, with a mortality rate of 40-50% within a few day...

Development of a Claims-Based Computable Phenotype for Ulcerative Colitis Flares

Several conditions exist that do not have their own unique diagnosis code in widely-used clinical terminologies, making them difficult to track and st...

Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases

Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma. ICU readmission in ICH patients is a...

Transformers Enhance the Predictive Power of Network Medicine

Self-attention mechanisms and token embeddings behind transformers allow the extraction of complex patterns from large datasets, and enhance the predi...

Galar - a large multi-label video capsule endoscopy dataset

Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitation...

Variable pharmacokinetics of coagulation factor VIII in the perioperative settting complicates personalisation of treatment in patients with haemophilia A

Pharmacokinetic (PK)-guided dosing of factor concentrates in patients with haemophilia A is generally recommended for the optimisation of prophylactic...

Predicting the stage of gastric cancer after gastrectomy based on machine learning algorithms

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...

Data-Driven Predictive Modeling for Massive Intraoperative Blood Loss during Liver Transplantation: Integrating Machine Learning Techniques

Massive intraoperative bleeding (IBL) in liver transplantation (LT) poses serious risks and strains healthcare resources necessitating better predicti...

Radiologist-AI workflow can be modified to reduce the risk of medical malpractice claims

Artificial Intelligence (AI) is rapidly changing the legal landscape of radiology. Results from a previous experiment suggested that providing AI erro...

CEREBLEED: Automated quantification and severity scoring of intracranial hemorrhage on non-contrast CT

Intracranial hemorrhage (ICH), whether spontaneous or traumatic, is a neurological emergency with high morbidity and mortality. Accurate assessment of...

AI-Driven Personalization of Dual Antiplatelet Therapy Duration Post-PCI: A Novel Approach Balancing Ischemic and Bleeding Risks

Precision-guided dual antiplatelet therapy (DAPT) duration post-percutaneous coronary intervention (PCI) remains a clinical challenge. Current risk st...

Prompt injection attacks on vision-language models for surgical decision support

Artificial Intelligence-driven analysis of laparoscopic video holds potential to increase the safety and precision of minimally invasive surgery. Visi...

Artificial Intelligence in Gastrointestinal Endoscopy: A Comprehensive Systematic Review

Artificial intelligence (AI) has emerged as a transformative force in gastrointestinal (GI) endoscopy, offering enhancements in diagnostic accuracy, l...