COVID-19

Auto informing COVID-19 detection result from x-ray/CT images based on deep learning.

It is no secret to all that the corona pandemic has caused a decline in all aspects of the world. Therefore, offering an accurate automatic diagnostic system is very important. This paper proposed an accurate COVID-19 system by testing various deep learning models for x-ray/computed tomography (CT) medical images. A deep preprocessing procedure was done with two filters and segmentation to increas...

Pathological Spectra of the Fisher Information Metric and Its Variants in Deep Neural Networks.

The Fisher information matrix (FIM) plays an essential role in statistics and machine learning as a Riemannian metric tensor or a component of the Hessian matrix of loss functions. Focusing on the FIM and its variants in deep neural networks (DNNs), we reveal their characteristic scale dependence on the network width, depth, and sample size when the network has random weights and is sufficiently w...

Prediction of driver variants in the cancer genome via machine learning methodologies.

Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a va...

Revisiting genome-wide association studies from statistical modelling to machine learning.

Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agric...

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network.

It is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistic...

Precise uncertain significance prediction using latent space matrix factorization models: genomics variant and heterogeneous clinical data-driven approaches.

Several studies to date have proposed different types of interpreters for measuring the degree of pathogenicity of variants. However, in predicting th...

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.

Although a wide variety of machine learning (ML) algorithms have been utilized to learn quantitative structure-activity relationships (QSARs), there i...

A deep learning approach for filtering structural variants in short read sequencing data.

Short read whole genome sequencing has become widely used to detect structural variants in human genetic studies and clinical practices. However, accu...

Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes.

SUMMARY: Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To fur...

Why do people oppose mask wearing? A comprehensive analysis of U.S. tweets during the COVID-19 pandemic.

OBJECTIVE: Facial masks are an essential personal protective measure to fight the COVID-19 (coronavirus disease) pandemic. However, the mask adoption ...

Transfer learning for mobile real-time face mask detection and localization.

OBJECTIVE: Due to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the h...

Research on automatic preprocessing equipment of salivary analyte for PCR inspection of coronavirus.

Polymerase chain reaction (PCR) inspection of salivary analyte is performed by pretreatment, RNA extraction setup, RNA extraction, PCR setup, and the ...

Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.

MOTIVATION: Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mec...

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study.

OBJECTIVE: The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a mode...

High-throughput developability assays enable library-scale identification of producible protein scaffold variants.

Proteins require high developability-quantified by expression, solubility, and stability-for robust utility as therapeutics, diagnostics, and in other...

A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study.

MATERIALS AND METHODS: Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. Af...

COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning.

Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 i...

Robotic-Assisted Surgery Training (RAST) Program: An Educational Research Protocol.

Technology has had a dramatic impact on how diseases are diagnosed and treated. Although cut, sew, and tie remain the staples of surgical craft, new t...

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holog...

VIDHOP, viral host prediction with deep learning.

MOTIVATION: Zoonosis, the natural transmission of infections from animals to humans, is a far-reaching global problem. The recent outbreaks of Zikavir...