COVID-19

DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development.

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, ...

Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study.

Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different popula...

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance ...

Characterization of PANoptosis-related genes in Crohn's disease by integrated bioinformatics, machine learning and experiments.

Currently, the biological understanding of Crohn's disease (CD) remains limited. PANoptosis is a revolutionary form of cell death reported to particip...

Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma.

Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microenvironment and early tumor development. Immunogenic...

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration.

Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complicati...

COVID‑19 detection from chest X-ray images using transfer learning.

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is it...

Stacked neural network for predicting polygenic risk score.

In recent years, the utility of polygenic risk scores (PRS) in forecasting disease susceptibility from genome-wide association studies (GWAS) results ...

Surveying haemoperfusion impact on COVID-19 from machine learning using Shapley values.

BACKGROUND: Haemoperfusion (HP) is an innovative extracorporeal therapy that utilizes special cartridges to filter the blood, effectively removing pro...

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk a...

WilsonGenAI a deep learning approach to classify pathogenic variants in Wilson Disease.

BACKGROUND: Advances in Next Generation Sequencing have made rapid variant discovery and detection widely accessible. To facilitate a better understan...

Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.

Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treat...

Automated Analysis of Split Kidney Function from CT Scans Using Deep Learning and Delta Radiomics.

Differential kidney function assessment is an important part of preoperative evaluation of various urological interventions. It is obtained through d...

The Surgical Clerkship in the COVID Era: A Natural Language Processing and Thematic Analysis.

INTRODUCTION: Responses to COVID-19 within medical education prompted significant changes to the surgical clerkship. We analyzed the changes in medica...

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. Th...

Multi-scale 3D-CRU for EEG emotion recognition.

In this paper, we propose a novel multi-scale 3D-CRU model, with the goal of extracting more discriminative emotion feature from EEG signals. By concu...

Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach.

Subcutaneous injections are an increasingly prevalent route of administration for delivering biological therapies including monoclonal antibodies (mAb...

D-TrAttUnet: Toward hybrid CNN-transformer architecture for generic and subtle segmentation in medical images.

Over the past two decades, machine analysis of medical imaging has advanced rapidly, opening up significant potential for several important medical ap...

Giving a Voice to Patients With Smell Disorders Associated With COVID-19: Cross-Sectional Longitudinal Analysis Using Natural Language Processing of Self-Reports.

BACKGROUND: Smell disorders are commonly reported with COVID-19 infection. The smell-related issues associated with COVID-19 may be prolonged, even af...

Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of appli...