Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

/pdf/applications-of-artificial-intelligence-in-battling-against-18cfgukc2t.pdf

Applications of artificial intelligence in battling against covid-19: A literature review.

While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.

The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis Automatic identification software tools are essential for helping to screen COVID-19 using CT images However, there are few datasets available, making it difficult to train deep learning (DL) networks To address this issue, a generative adversarial network (GAN) is proposed in this work to generate more CT images The Whale Optimization Algorithm (WOA) is used to optimize the hyperparameters of GAN's generator The proposed method is tested and validated with different classification and meta-heuristics algorithms using the SARS-CoV-2 CT-Scan dataset, consisting of COVID-19 and non-COVID-19 images The performance metrics of the proposed optimized model, including accuracy (9922%), sensitivity (9978%), specificity (9778%), F1-score (9879%), positive predictive value (9782%), and negative predictive value (9977%), as well as its confusion matrix and receiver operating characteristic (ROC) curves, indicate that it performs better than state-of-the-art methods This proposed model will help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks

/pdf/automatic-screening-of-covid-19-using-an-optimized-4ksqh6d3rj.pdf

Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network.

COVID-19 has infected 223 countries and caused 2.8 million deaths worldwide (at the time of writing this article), and the death rate is increasing continuously. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome the rapid spread of the deadly virus in any geographical area. In this situation, the previous epidemic evidence on Machine Learning (ML) and Deep Learning (DL) techniques encouraged the researchers to play a significant role in detecting COVID-19. Similarly, the rising scope of ML/DL methodologies in the medical domain also advocates its significant role in COVID-19 detection. This systematic review presents ML and DL techniques practiced in this era to predict, diagnose, classify, and detect the coronavirus. In this study, the data was retrieved from three prevalent full-text archives, i.e., Science Direct, Web of Science, and PubMed, using the search code strategy on 16 March 2021. Using professional assessment, among 961 articles retrieved by an initial query, only 40 articles focusing on ML/DL-based COVID-19 detection schemes were selected. Findings have been presented as a country-wise distribution of publications, article frequency, various data collection, analyzed datasets, sample sizes, and applied ML/DL techniques. Precisely, this study reveals that ML/DL technique accuracy lay between 80% to 100% when detecting COVID-19. The RT-PCR-based model with Support Vector Machine (SVM) exhibited the lowest accuracy (80%), whereas the X-ray-based model achieved the highest accuracy (99.7%) using a deep convolutional neural network. However, current studies have shown that an anal swab test is super accurate to detect the virus. Moreover, this review addresses the limitations of COVID-19 detection along with the detailed discussion of the prevailing challenges and future research directions, which eventually highlight outstanding issues.

https://www.mdpi.com/2076-3417/11/8/3414/pdf

COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review

Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.

One of the most important SARS-CoV-2 protein targets for therapeutics is the 3C-like protease (main protease, Mpro) In our previous work1â€‹we used the first Mpro crystal structure to become available, 6LU7 On February 4, 2020 Insilico Medicine released the first potential novel protease inhibitors designed using a â€‹de novo,â€‹AI-driven generative chemistry approach Nearly 100 X-ray structures of Mpro co-crystallized both with covalent and non-covalent ligands have been published since then Here we utilize the recently published 6W63 crystal structure of Mpro complexed with a non-covalent inhibitor and combined two approaches used in our previous study: ligand-based and crystal structure-based We published 10 representative structures for potential development with 3D representation in PDformat and welcome medicinal chemists for broad discussion and generated output analysis The molecules in SDF format and PDB-models for generated protein-ligand complexes are available here and at https://insilico com/ncov-sprint/ â€‹Medicinal chemistry VR analysis was provided by â€‹Nanome team and the video of VR session is available at â€‹https://bit ly/ncov-vr â€‹ / / / / /div

/pdf/potential-non-covalent-sars-cov-2-3c-like-protease-29r7wflkpa.pdf

Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative DeeLearning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

The proposed model is a Variational Autoencoder having a learnable prior that is parametrized with a Tensor Train (VAE-TTLP). The VAE-TTLP can be used to generate new objects, such as molecules, that have specific properties and that can have specific biological activity (when a molecule). The VAE-TTLP can be trained in a way with the Tensor Train so that the provided data may omit one or more properties of the object, and still result in an object with a desired property.

Subset conditioning using variational autoencoder with a learnable tensor train induced prior

A method for generating an object includes: providing a dataset having object data and condition data; processing the object data to obtain latent object data and latent object-condition data; processing the condition data to obtain latent condition data and latent condition-object data; processing the latent object data and the latent object-condition data to obtain generated object data; processing the latent condition data and latent condition-object data to obtain generated condition data; comparing the latent object-condition data to the latent condition-object data to determine a difference; processing the latent object data and latent condition data and one of the latent object-condition data or latent condition-object data to obtain a discriminator value; and selecting a selected object based on the generated object data.

Zhebrak Alexander

Papers

Potential Non-Covalent SARS-CoV-2 3C-like Protease Inhibitors Designed Using Generative DeeLearning Approaches and Reviewed by Human Medicinal Chemist in Virtual Reality

Subset conditioning using variational autoencoder with a learnable tensor train induced prior

Mutual information adversarial autoencoder