Infectious Disease

Attention-Guided Convolutional Neural Network for Detecting Pneumonia on Chest X-Rays.

Pneumonia is a common infectious disease in the world. Its main diagnostic method is chest X-ray (CXR) examination. However, the high visual similarity between a large number of pathologies in CXR makes the interpretation and differentiation of pneumonia a challenge. In this paper, we propose an improved convolutional neural network (CNN) model for pneumonia detection. In order to guide the CNN to...

Fully Automated Segmentation of Alveolar Bone Using Deep Convolutional Neural Networks from Intraoral Ultrasound Images.

Delineation of alveolar bone aids the diagnosis and treatment of periodontal diseases. In current practice, conventional 2D radiography and 3D cone-beam computed tomography (CBCT) imaging are used as the non-invasive approaches to image and delineate alveolar bone structures. Recently, high-frequency ultrasound imaging is proposed as an alternative to conventional imaging methods to prevent the ha...

BacPaCS-Bacterial Pathogenicity Classification via Sparse-SVM.

MOTIVATION: Bacterial infections are a major cause of illness worldwide. However, most bacterial strains pose no threat to human health and may even b...

DeepT3: deep convolutional neural networks accurately identify Gram-negative bacterial type III secreted effectors using the N-terminal sequence.

MOTIVATION: Various bacterial pathogens can deliver their secreted substrates also called effectors through Type III secretion systems (T3SSs) into ho...

Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.

MOTIVATION: Antibiotic resistance constitutes a major public health crisis, and finding new sources of antimicrobial drugs is crucial to solving it. B...

Robot-Based Measures of Upper Limb Cognitive-Motor Interference Across the HIV-Stroke Spectrum.

Robot-based neurorehabilitation strategies often ignore cognitive performance during treatment, but this is a need in populations dealing with a wide ...

New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research.

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.

In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Man...

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study.

BACKGROUND: Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised mac...

High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.

One potential source of new antibacterials is through probing existing chemical libraries for copper-dependent inhibitors (CDIs), i.e., molecules with...

Artificial intelligence estimates the impact of human papillomavirus types in influencing the risk of cervical dysplasia recurrence: progress toward a more personalized approach.

The objective of this study was to determine whether the pretreatment human papillomavirus (HPV) genotype might predict the risk of cervical dysplasia...

Fuzzy logic and hospital admission due to respiratory diseases using estimated values by mathematical model.

Hospitalizations due to respiratory diseases generate financial costs for the Health System in addition to social costs. Objective of this study was t...

4mCPred: machine learning methods for DNA N4-methylcytosine sites prediction.

MOTIVATION: N4-methylcytosine (4mC), an important epigenetic modification formed by the action of specific methyltransferases, plays an essential role...

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving...

Predicting culturable enterococci exceedances at Escambron Beach, San Juan, Puerto Rico using satellite remote sensing and artificial neural networks.

Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monit...

An account of in silico identification tools of secreted effector proteins in bacteria and future challenges.

Bacterial pathogens secrete numerous effector proteins via six secretion systems, type I to type VI secretion systems, to adapt to new environments or...

Predictive analysis across spatial scales links zoonotic malaria to deforestation.

The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human in...

Victors: a web-based knowledge base of virulence factors in human and animal pathogens.

Virulence factors (VFs) are molecules that allow microbial pathogens to overcome host defense mechanisms and cause disease in a host. It is critical t...

Predicting Influenza A Tropism with End-to-End Learning of Deep Networks.

The type of host that a virus can infect, referred to as host specificity or tropism, influences infectivity and thus is important for disease diagnos...

Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making.

This review critically analyzes how machine learning is being used to support clinical decision-making in the management of potentially resectable pan...