Bacterial Infection

Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method.

Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVI...

Predicting the performance of anaerobic digestion using machine learning algorithms and genomic data.

Modeling of anaerobic digestion (AD) is crucial to better understand the process dynamics and to improve the digester performance. This is an essential yet difficult task due to the complex and unknown interactions within the system. The application of well-developed data mining technologies, such as machine learning (ML) and microbial gene sequencing techniques are promising in overcoming these c...

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to...

Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant.

IMPORTANCE: Sepsis disproportionately affects recipients of allogeneic hematopoietic cell transplant (allo-HCT), and timely detection is crucial. Howe...

Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.

The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PP...

Deep Learning for Novel Antimicrobial Peptide Design.

Antimicrobial resistance is an increasing issue in healthcare as the overuse of antibacterial agents rises during the COVID-19 pandemic. The need for ...

A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach.

Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other pe...

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency ...

Synthesis and antimicrobial activities of chitosan/polypropylene carbonate-based nanoparticles.

Antibiotic resistance is an emerging threat to public health. The development of a new generation of antimicrobial compounds is therefore currently re...

Antibiofilm Activity of α-Amylase from Bacillus subtilis and Prediction of the Optimized Conditions for Biofilm Removal by Response Surface Methodology (RSM) and Artificial Neural Network (ANN).

α-amylase is known to have antibiofilm activity against biofilms of both Gram positive and Gram-negative bacterial strains. Partially purified α-amyla...

Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.

Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films bri...

AI-based mobile application to fight antibiotic resistance.

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic suscept...

The role of artificial intelligence in the battle against antimicrobial-resistant bacteria.

Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy develo...

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute r...

Biohybrid robotics: From the nanoscale to the macroscale.

Biohybrid robotics is a field in which biological entities are combined with artificial materials in order to obtain improved performance or features ...

A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis.

UV-visible spectroscopy (UV-Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of micr...

Rotatable central composite design versus artificial neural network for modeling biosorption of Cr by the immobilized Pseudomonas alcaliphila NEWG-2.

Heavy metals, including chromium, are associated with developed industrialization and technological processes, causing imbalanced ecosystems and sever...

Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.

The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. Thi...

Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases.

Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is ...

iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.

Identification of bacterial type III secreted effectors (T3SEs) has become a popular research topic in the field of bioinformatics due to its crucial ...