COVID-19

Interpretation of Structure-Activity Relationships in Real-World Drug Design Data Sets Using Explainable Artificial Intelligence.

In silico models based on Deep Neural Networks (DNNs) are promising for predicting activities and properties of new molecules. Unfortunately, their inherent black-box character hinders our understanding, as to which structural features are important for activity. However, this information is crucial for capturing the underlying structure-activity relationships (SARs) to guide further optimization....

Low-degree term first in ResNet, its variants and the whole neural network family.

To explain the working mechanism of ResNet and its variants, this paper proposes a novel argument of shallow subnetwork first (SSF), essentially low-degree term first (LDTF), which also applies to the whole neural network family. A neural network with shortcut connections behaves as an ensemble of a number of subnetworks of differing depths. Among the subnetworks, the shallow subnetworks are train...

The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection.

Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There ...

Contour proposal networks for biomedical instance segmentation.

We present a conceptually simple framework for object instance segmentation, called Contour Proposal Network (CPN), which detects possibly overlapping...

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complem...

Automated detection of COVID-19 through convolutional neural network using chest x-ray images.

The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldw...

COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization.

The coronavirus outbreak 2019, called COVID-19, which originated in Wuhan, negatively affected the lives of millions of people and many people died fr...

Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer.

This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed w...

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the found...

COVID-19 Mask Usage and Social Distancing in Social Media Images: Large-scale Deep Learning Analysis.

BACKGROUND: The adoption of nonpharmaceutical interventions and their surveillance are critical for detecting and stopping possible transmission route...

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in gene...

Objective evaluation of deep uncertainty predictions for COVID-19 detection.

Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and oth...

StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.

Whole-genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unres...

Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?

PURPOSE: Over the last 2 years, the artificial intelligence (AI) community has presented several automatic screening tools for coronavirus disease 201...

PRCTC: a machine learning model for prediction of response to corticosteroid therapy in COVID-19 patients.

Corticosteroid has been proved to be one of the few effective treatments for COVID-19 patients. However, not all the patients were suitable for cortic...

Meta-analysis of human prediction error for incentives, perception, cognition, and action.

Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species...

Feedforward backpropagation artificial neural networks for predicting mechanical responses in complex nonlinear structures: A study on a long bone.

Feedforward backpropagation artificial neural networks (ANNs) have been increasingly employed in many engineering practices concerning materials model...

Rapid video-based deep learning of cognate versus non-cognate T cell-dendritic cell interactions.

Identification of cognate interactions between antigen-specific T cells and dendritic cells (DCs) is essential to understanding immunity and tolerance...

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients.

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patient...

Forty years of directed evolution and its continuously evolving technology toolbox: A review of the patent landscape.

Generating functional protein variants with novel or improved characteristics has been a goal of the biotechnology industry and life sciences, for dec...