COVID-19

Opportunities and challenges for the computational interpretation of rare variation in clinically important genes.

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance ...

The value of federated learning during and post-COVID-19.

Federated learning (FL) as a distributed machine learning (ML) technique has lately attracted increasing attention of healthcare stakeholders as FL is perceived as a promising decentralized approach to address data privacy and security concerns. The FL approach stores and maintains the privacy-sensitive data locally while allows multiple sites to train ML models collaboratively. We aim to describe...

FLANNEL (Focal Loss bAsed Neural Network EnsembLe) for COVID-19 detection.

OBJECTIVE: The study sought to test the possibility of differentiating chest x-ray images of coronavirus disease 2019 (COVID-19) against other pneumon...

Use of Telepresence Robots in Glaucoma Patient Education.

PRCIS: Telepresence robots (TR) present the versatility to effectively provide remote educational sessions for patients affected by glaucoma to improv...

COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model.

The primary purpose of this research is to identify the best COVID-19 mortality model for India using regression models and is to estimate the future ...

Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States.

This study examines the role that racial residential segregation has played in shaping the spread of COVID-19 in the United States as of September 30,...

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset.

OBJECTIVE: The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data s...

Using machine learning to investigate the public's emotional responses to work from home during the COVID-19 pandemic.

According to event system theory (EST; Morgeson et al., Academy of Management Review, 40, 2015, 515-537), the coronavirus disease 2019 (COVID-19) pand...

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We ...

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological u...

Mouse Genome Database (MGD): Knowledgebase for mouse-human comparative biology.

The Mouse Genome Database (MGD; http://www.informatics.jax.org) is the community model organism knowledgebase for the laboratory mouse, a widely used ...

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.

BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidiscipl...

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.

PURPOSE: Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assi...

Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.

BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological co...

Can occupational therapy manpower be replaced with social robots in a singing group during COVID-19?

BACKGROUND: Prior to the COVID-19 global health emergency, reducing direct contacts between therapists and patients is an important issue, and could b...

Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters.

High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein-protein inte...

Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review.

BACKGROUND: This paper provides a systematic review of the application of Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep L...

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this...

Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning.

Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical targ...