Infectious Disease

Deep Bidirectional Classification Model for COVID-19 Disease Infected Patients.

In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed Tomography (CT) has proven to be a useful supplement to reverse transcription polymerase chain reaction (RT-PCR) and has been shown to have high sensitivity to diagnose this condition. Therefore, radiologi...

LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network.

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR....

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease pro...

Deep learning-based detection of eosinophilic esophagitis.

BACKGROUND: For eosinophilic esophagitis (EoE), a substantial diagnostic delay is still a clinically relevant phenomenon. Deep learning-based algorith...

Machine Learning Aids Classification and Discrimination of Noncanonical DNA Folding Motifs by an Arrayed Host:Guest Sensing System.

An arrayed host:guest fluorescence sensor system can discriminate among and classify multiple different noncanonical DNA structures by exploiting sele...

Deep Learning to Determine the Activity of Pulmonary Tuberculosis on Chest Radiographs.

Background Determining the activity of pulmonary tuberculosis on chest radiographs is difficult. Purpose To develop a deep learning model to identify ...

Fully automated unified prognosis of Covid-19 chest X-ray/CT scan images using Deep Covix-Net model.

SARS-COV2 (Covid-19) prevails in the form of multiple mutant variants causing pandemic situations around the world. Thus, medical diagnosis is not acc...

RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection.

The novel 2019 Coronavirus (COVID-19) infection has spread worldwide and is currently a major healthcare challenge around the world. Chest computed to...

Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19.

The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step...

Editorial: The National COVID Cohort Collaborative Consortium Combines Population Data with Machine Learning to Evaluate and Predict Risk Factors for the Severity of COVID-19.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pne...

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal ...

Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network.

BACKGROUND: The recent emergence of a highly infectious and contagious respiratory viral disease known as COVID-19 has vastly impacted human lives and...

Design, synthesis and mechanistic study of new benzenesulfonamide derivatives as anticancer and antimicrobial agents carbonic anhydrase IX inhibition.

Changes in gene expression cause uncontrolled cell proliferation and consequently tumor hypoxia. The tumor cells shift their metabolism to anaerobic g...

Predicting mutant outcome by combining deep mutational scanning and machine learning.

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experi...

Co-AMPpred for in silico-aided predictions of antimicrobial peptides by integrating composition-based features.

BACKGROUND: Antimicrobial peptides (AMPs) are oligopeptides that act as crucial components of innate immunity, naturally occur in all multicellular or...

Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients.

BACKGROUND: In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the heal...

Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review.

Research on affective communication for socially assistive robots has been conducted to enable physical robots to perceive, express, and respond emoti...

Real-time interactive artificial intelligence of things-based prediction for adverse outcomes in adult patients with pneumonia in the emergency department.

BACKGROUND: Artificial intelligence of things (AIoT) may be a solution for predicting adverse outcomes in emergency department (ED) patients with pneu...

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network.

BACKGROUND: COVID-19 diagnosis in symptomatic patients is an important factor for arranging the necessary lifesaving facilities like ICU care and vent...

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study.

BACKGROUND: Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection ...