COVID-19

FLYNC: a machine-learning-driven framework for discovering long noncoding RNAs in Drosophila melanogaster.

Noncoding RNAs have increasingly recognized roles in critical molecular mechanisms of disease. However, the noncoding genome of Drosophila melanogaster, one of the most powerful disease model organisms, has been understudied. Here, we present FLYNC-FLY noncoding RNA discovery and classification-a novel explainable boosting machine model that accurately predicts the probability of a newly identifie...

Explainable AI for pain perception: subject-independent EEG decoding using DeepSHAP and CNNs.

Objective.Accurate classification of pain levels is essential for clinical monitoring, particularly in clinical populations with limited verbal communication. This study explores the feasibility of decoding pain from EEG using explainable deep learning.Approach.EEG signals from 50 subjects exposed to low and high pain stimuli were analyzed. A 1D convolutional neural network (CNN) was trained using...

Prognostic Significance of Computed Tomography Severity Score for Machine Learning Prediction of Intensive Care Unit Admission in COVID-19 Patients.

OBJECTIVE: The computed tomography-severity score (CT-SS) quantifies the severity of pulmonary involvement and is significantly associated with diseas...

BreastDCEDL: A standardized deep learning-ready breast DCE-MRI dataset of 2,070 patients.

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is essential for monitoring breast cancer treatment response, yet deep learning progres...

Mechanism of astaxanthin improving exercise-induced muscle damage: an integrated approach using network pharmacology, molecular dynamics simulation, and experimental validation.

UNLABELLED: This research evaluated the potential mechanisms of ASX on EIMD. The network pharmacology, machine learning, and transcription sequencing ...

Performance analysis of liver segmentation using nn-UNet TotalSegmentator: Focus on atypical livers, pathologies, and variants.

RATIONALE AND OBJECTIVES: This study evaluates the accuracy of the nn-UNet TotalSegmentator (TS) by Wasserthal et al. (2023) in segmenting atypical li...

Integrating CAR-T therapy with PD-1/PD-L1 blockade: Mechanisms, synergy, and optimized strategies in NSCLC.

Non-small cell lung cancer (NSCLC) presents persistent challenges in immunotherapy, as the clinical benefit of programmed cell death protein 1 (PD-1) ...

Multi-attribute methods in antibody-drug conjugate characterization and quality control: Opportunities, challenges, and regulatory outlook.

Antibody-drug conjugates (ADCs) are complex biotherapeutics that combine the selectivity of monoclonal antibodies with the potency of cytotoxic payloa...

Optimization and validation of multiscale feature selection for EEG-based recognition of drivers' negative emotions.

OBJECTIVE: Negative emotions, such as stress and anger, are significant factors leading to dangerous driving behavior. Investigating the impact of the...

Machine learning and multi-omics integration identifies immunological predictors and mechanistic insights in autoimmune encephalitis.

OBJECTIVE: To develop an interpretable prognostic prediction model for autoimmune encephalitis (AE) using immunological indicators and to investigate ...

Challenges and Opportunities with CRISPR-Based Genome Editing in Legume Crops.

Over the last couple of decades, tremendous progress has been made in legume genomics. Genomics information generated for legume crops is being explor...

Artificial intelligence applications in ovarian cancer detection: A systematic literature review of deep learning approaches and clinical translation challenges.

This systematic literature review examines artificial intelligence applications in ovarian cancer detection through analysis of 61 studies published b...

Components of metabolic syndrome affect COVID-19 outcomes via platelet aggregation.

BACKGROUND: Emerging evidence suggests that platelet activation and aggregation are common factors in both metabolic syndrome (MetS) and severe COVID-...

A predictive framework for stop-loss variants with C-terminal extensions.

Stop codons dictate translation termination, and variants occurring at these sites can result in stop-loss variants, leading to C-terminal extensions ...

PMNO: A novel physics guided multi-step neural operator predictor for partial differential equations.

Neural operators, which aim to approximate mappings between infinite-dimensional function spaces, have been widely applied in the simulation and predi...

Stem Cell-Derived Organoids as a Next-Generation Platform for Drug Toxicity and Efficacy Testing: Toward Replacing Animal Models in Regulatory Science.

Three-dimensional, self-organizing structures derived from stem cells, known as organoids, represent a groundbreaking advancement in preclinical drug ...

Programmable Smart CAR-T design: A new paradigm in precision immunotherapy driven by logic gates, conditional activation, allogeneic strategies, and artificial intelligence.

Chimeric antigen receptor T-cell (CAR-T) therapy has achieved unprecedented success in hematological malignancies but faces formidable challenges in s...

Identifying endoplasmic reticulum stress-related key genes of vascular dementia using bioinformatics and biological experiments.

Vascular dementia (VaD) is the second most common type of dementia, yet its pathogenesis is not fully understood, and effective diagnostic and therape...

RoGAtten: Rotary gated linear attention for multivariate time series forecasting.

Thousands of network nodes in the Internet of Things produce vast amounts of long-term time series. Predicting network traffic helps identify security...

Machine learning-based identification of factors associated with spontaneous abortion in patients with Systemic lupus erythematosus (SLE): Insights from the Egyptian College of Rheumatology (ECR)-SLE cohort.

BackgroundSystemic lupus erythematosus (SLE), an autoimmune disease, predominantly affects women and is associated with an increased risk of spontaneo...