COVID-19

Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.

BACKGROUND: Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms of...

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences,...

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-st...

Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling.

Mathematical modeling of epidemic dynamics is crucial to understand its underlying mechanisms, quantify important parameters, and make predictions tha...

Molybdenum Disulfide-Assisted Spontaneous Formation of Multistacked Gold Nanoparticles for Deep Learning-Integrated Surface-Enhanced Raman Scattering.

Several fabrication methods have been developed for label-free detection in various fields. However, fabricating high-density and highly ordered nanos...

Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker.

Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either c...

RAIN: machine learning-based identification for HIV-1 bNAbs.

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importan...

A tree-based explainable AI model for early detection of Covid-19 using physiological data.

With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificia...

Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning.

The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients' care and resource allocation can be ...

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immuno...

A hybrid human recognition framework using machine learning and deep neural networks.

Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus ...

Predicting Depression, Anxiety, and Their Comorbidity among Patients with Breast Cancer in China Using Machine Learning: A Multisite Cross-Sectional Study.

Depression and anxiety are highly prevalent among patients with breast cancer. We tested the capacity of personal resources (psychological resilience,...

Building a challenging medical dataset for comparative evaluation of classifier capabilities.

Since the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual dat...

A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection.

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reli...

Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection.

Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of overfitting, where small training data sets can result...

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. Thi...

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways...

Development of a real-time cattle lameness detection system using a single side-view camera.

Recent advancements in machine learning and deep learning have revolutionized various computer vision applications, including object detection, tracki...

Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study.

BACKGROUND: Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 75...

Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.

Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico scor...