Infectious Disease

Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors.

MOTIVATION: Identification of virulence factors (VFs) is critical to the elucidation of bacterial pathogenesis and prevention of related infectious diseases. Current computational methods for VF prediction focus on binary classification or involve only several class(es) of VFs with sufficient samples. However, thousands of VF classes are present in real-world scenarios, and many of them only have ...

Advancing PICO element detection in biomedical text via deep neural networks.

MOTIVATION: In evidence-based medicine, defining a clinical question in terms of the specific patient problem aids the physicians to efficiently identify appropriate resources and search for the best available evidence for medical treatment. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical tex...

Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic.

PURPOSE OF REVIEW: We review applications of artificial intelligence (AI), including machine learning (ML), in the field of HIV prevention.

Computer-aided diagnosis in the era of deep learning.

Computer-aided diagnosis (CAD) has been a major field of research for the past few decades. CAD uses machine learning methods to analyze imaging and/o...

Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis.

BACKGROUND/AIM: To evaluate the research trends in coronavirus disease (COVID-19).

Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods.

Characterization of decision-making in cells in response to received signals is of importance for understanding how cell fate is determined. The probl...

Machine learning predicts new anti-CRISPR proteins.

The increasing use of CRISPR-Cas9 in medicine, agriculture, and synthetic biology has accelerated the drive to discover new CRISPR-Cas inhibitors as p...

Artificial intelligence and the future of global health.

Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised ho...

Comparative outcomes of robot-assisted minimally invasive versus open esophagectomy in patients with esophageal squamous cell carcinoma: a propensity score-weighted analysis.

Robots are increasingly used in minimally invasive surgery. We evaluated the clinical benefits of robot-assisted minimally invasive esophagectomy (RAM...

Deep Learning Localization of Pneumonia: 2019 Coronavirus (COVID-19) Outbreak.

AI Surveillance during Pandemics: Ethical Implementation Imperatives.

Artificial intelligence surveillance can be used to diagnose individual cases, track the spread of Covid-19, and help provide care. The use of AI for ...

Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy.

OBJECTIVE: To develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV).

Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.

Each influenza pandemic was caused at least partly by avian- and/or swine-origin influenza A viruses (IAVs). The timing of and the potential IAVs invo...

Combating COVID-19-The role of robotics in managing public health and infectious diseases.

COVID-19 may drive sustained research in robotics to address risks of infectious diseases.

Deep learning guided image-based droplet sorting for on-demand selection and analysis of single cells and 3D cell cultures.

Uncovering the heterogeneity of cellular populations and multicellular constructs is a long-standing goal in fields ranging from antimicrobial resista...

Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda.

INTRODUCTION: Real-time electronic adherence monitoring (EAM) systems could inform on-going risk assessment for HIV viraemia and be used to personaliz...

On classifying sepsis heterogeneity in the ICU: insight using machine learning.

OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of t...

[Prognostic model of small sample critical diseases based on transfer learning].

Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large p...

A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we traine...

PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.

MOTIVATION: Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important ro...