COVID-19

Pandemic-Potential Viruses are a Blind Spot for Frontier Open-Source LLMs

We study large language models (LLMs) for front-line, pre-diagnostic infectious-disease triage, a critically understudied stage in clinical interventions, public health, and biothreat containment. We focus specifically on the operational decision of classifying symptomatic cases as viral vs. non-viral at first clinical contact, a critical decision point for resource allocation, quarantine strategy...

Machine learning-driven prediction of opioid and stimulant-related drug overdose fatalities: Analysis of the potential fourth wave

Between 2010 and 2021, fentanyl and stimulants co-involved deaths increased from 0.6% to 32.3% of all overdose deaths in the U.S. The Centers for Disease Control and Prevention monitors overdose deaths, but reports are delayed by about 4 -6 months. Therefore, advanced methods are needed for optimized trend monitoring and preparing the healthcare system. We developed and compared traditional and ma...

CrosSplice: A Pipeline for Identifying Rare Splice-Site Creating Variants from Cross-Tissue Transcriptome Data

Despite their profound impact on patients’ lives, most rare and intractable diseases still lack established treatments. Genomic variants that disrupt ...

ML-Guided GWAS Reveals Genetic Architectures for MASLD for Overweight and Lean Individuals in the All of Us Cohort

Metabolic dysfunction-associated steatotic liver disease (MASLD) arises from excessive hepatic fat accumulation that triggers inflammation and liver i...

Multitask Artificial Intelligence–Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs

Preoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing ...

Machine Learning and Micro Capture-C resolve GWAS associations revealing endothelial stress pathways in Coronary Artery Disease

Resolving the gene targets of non-coding genetic variation is the major bottleneck in translating genome wide association studies into mechanistic und...

Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease Heritability

The majority of causal genome-wide association studies (GWAS) variants for Alzheimer’s disease (AD) are believed to reside in noncoding regions of the...

Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion

The emergence of generative AI and controllable diffusion has made image-to-image synthesis increasingly practical and efficient. However, when inpu...

Foreground-Covering Prototype Generation and Matching for SAM-Aided Few-Shot Segmentation

We propose Foreground-Covering Prototype Generation and Matching to resolve Few-Shot Segmentation (FSS), which aims to segment target regions in unl...

Deciphering mitochondrial dysfunction in keratoconus: Insights into ACSL4 from machine learning-based bulk and single-cell transcriptome analyses and experimental validation.

Keratoconus (KC) is a prevalent ectatic corneal disease and the leading cause of corneal transplantation globally. Despite evidence of mitochondrial a...

Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers.

The mortality rate is very high in patients with severe COVID-19. Nearly 32% of COVID-19 patients are critically ill, with mortality rates ranging fr...

Pyroptosis-Related Gene Signatures Enable Robustly Diagnosis, Prognosis and Immune Responses Prediction in Uterine Corpus Endometrial Carcinoma.

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignancy with poor prognosis and high lethality rates. Pyroptosis, a pro-inflammator...

Intelligent penetration testing method for power internet of things systems combining ontology knowledge and reinforcement learning.

With the application of new-generation information technologies such as big data, artificial intelligence, and the energy Internet in Power Internet o...

The role of product art design based on a fuzzy decision support system in improving user interaction experience.

User interaction for product selection relies on its design and technical support to improve the quality of the experience. Decision support systems a...

Adaptive mechanism-based grey wolf optimizer for feature selection in high-dimensional classification.

Feature Selection (FS) is a crucial component of machine learning and data mining. Its goal is to eliminate redundant and irrelevant features from a d...

Modeling protective meningococcal antibody responses and factors influencing antibody persistence following vaccination with MenAfriVac using machine learning.

Meningococcal meningitis poses a significant public health burden in the meningitis belt region of sub-Saharan Africa. The introduction of the meningo...

iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.

BACKGROUND: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infec...

The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.

Nucleic acid tests (NATs) are considered as gold standard in molecular diagnosis. To meet the demand for onsite, point-of-care, specific and sensitive...

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and c...

Leveraging artificial intelligence to promote COVID-19 appropriate behaviour in a healthcare institution from north India: A feasibility study.

Background & Objectives Non-pharmacological interventions (NPI) were crucial in curbing the initial COVID-19 pandemic waves, but compliance was diffic...