Infectious Disease

The design of compounds with desirable properties - The anti-HIV case study.

Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various...

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging.

In assessing food microbial safety, the presence of Escherichia coli is a critical indicator of fecal contamination. However, conventional detection methods require the isolation of bacterial macrocolonies for biochemical or genetic characterization, which takes a few days and is labor-intensive. In this study, we show that the real-time object detection and classification algorithm You Only Look ...

BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations.

Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for observing individual cel...

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by hum...

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength a...

Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides.

In this study, we propose , a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of t...

Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach.

The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a li...

Automation of yeast spot assays using an affordable liquid handling robot.

The spot assay of the budding yeast Saccharomyces cerevisiae is an experimental method that is used to evaluate the effect of genotypes, medium condit...

Prediction of Equipment Effectiveness using Hybrid Moving Average-Adaptive Neuro Fuzzy Inference System (MA-ANFIS) for decision support in Bus Body Building Industry.

Managers are driven to accomplish significantly higher levels of operational performance due to the difficulty of today's dynamic production environme...

Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN.

We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between ...

RMSCNN: A Random Multi-Scale Convolutional Neural Network for Marine Microbial Bacteriocins Identification.

The abuse of traditional antibiotics has led to an increase in the resistance of bacteria and viruses. Similar to the function of antibacterial peptid...

OnAI-Comp: An Online AI Experts Competing Framework for Early Sepsis Detection.

Sepsis is a major public concern due to its high mortality, morbidity, and financial cost. There are many existing works of early sepsis prediction us...

Accurate identification of living Bacillus spores using laser tweezers Raman spectroscopy and deep learning.

Accurately, rapidly, and noninvasively identifying Bacillus spores can greatly contribute to controlling a plenty of infectious diseases. Laser tweeze...

Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models.

Antibiotic-resistant bacteria and antibiotic resistance genes (ARGs) are pollutants of worldwide concern that seriously threaten public health and eco...

Geographical classification of malaria parasites through applying machine learning to whole genome sequence data.

Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium viv...

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray.

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learnin...

Considerations and Challenges for Real-World Deployment of an Acoustic-Based COVID-19 Screening System.

Coronavirus disease 2019 (COVID-19) has led to countless deaths and widespread global disruptions. Acoustic-based artificial intelligence (AI) tools c...

Minimally Invasive Lower Anterior Resections - Better than Open But Not All the Same.

Optimal approach for lower anterior resection has been closely debated. The relatively new addition of the robotic approach adds a layer of complexit...

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification.

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease contr...

Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database.

Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of ...