Bacterial Infection

Deep Learning for Discrimination Between Fungal Keratitis and Bacterial Keratitis: DeepKeratitis.

PURPOSE: Microbial keratitis is an urgent condition in ophthalmology that requires prompt treatment. This study aimed to apply deep learning algorithms for rapidly discriminating between fungal keratitis (FK) and bacterial keratitis (BK).

Systematic TB screening using WHO radiograph categorisation and care outcomes.

An appropriate screening approach and quality care are crucial for TB programmes in prisons. This study assessed crude TB prevalence, accuracy of the screening methods and treatment outcomes in a Thai prison. This was a retrospective analysis of findings from a mass CXR screening conducted among incarcerated people in July 2017. Digital radiographs were forwarded to a chest physician to read and ...

ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.

Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to...

Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes.

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such...

Automated extraction of genes associated with antibiotic resistance from the biomedical literature.

The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phen...

Predicting Type III Effector Proteins Using the Effectidor Web Server.

Various Gram-negative bacteria use secretion systems to secrete effector proteins that manipulate host biochemical pathways to their benefit. We and o...

A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.

Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased....

Machine learning for identifying resistance features of using whole-genome sequence single nucleotide polymorphisms.

, a gram-negative bacterium, is a common pathogen causing nosocomial infection. The drug-resistance rate of is increasing year by year, posing a sev...

Bacteria Shape Classification using Small-Scale Depthwise Separable CNNs.

Fast detection and classification of bacteria species play a crucial role in modern clinical microbiology systems. These processes are often performed...

Deep Learning Assisted Microfluidic Impedance Flow Cytometry for Label-free Foodborne Bacteria Analysis and Classification.

According to the urgent need for rapid detection and identification of foodborne bacteria to prevent public health event, a microfluidic electrical im...

Accurate prediction of multi-label protein subcellular localization through multi-view feature learning with RBRL classifier.

Multi-label proteins can participate in carrier transportation, enzyme catalysis, hormone regulation and other life activities. Meanwhile, they play a...

Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec.

The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant ...

DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases.

Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. ...

Prediction of prokaryotic transposases from protein features with machine learning approaches.

Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA...

Predicting Antibiotic Resistance in Hospitalized Patients by Applying Machine Learning to Electronic Medical Records.

BACKGROUND: Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning (ML) algor...

DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.

Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, ...

[A preliminary investigation on a deep learning convolutional neural networks based pulmonary tuberculosis CT diagnostic model].

To evaluate the clinical value of a pulmonary tuberculosis CT diagnostic model based on deep learning convolutional neural networks (CNN). From Marc...

Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.

Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many type...

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated ge...

Structures of the β-barrel assembly machine recognizing outer membrane protein substrates.

β-barrel outer membrane proteins (β-OMPs) play critical roles in nutrition acquisition, protein import/export, and other fundamental biological proces...