Infectious Disease

Deformable attention (DANet) for semantic image segmentation.

Deep learning based medical image segmentation is currently a widely researched topic. Attention mechanism used with deep networks significantly benefit semantic segmen-tation tasks. The recent criss-cross-attention module captures global self-attention while remaining memory and time efficient. However, capturing attention from only the pertinent non-local locations can cardinally boost the accur...

Design and development of a social assistive robot for music and game activities: a case study in a residential facility for disabled people.

Cognitive disability strongly reduces people's autonomy in performing desired as well as daily activities. The use of Social Assistive Robots (SARs) for cognitive rehabilitation therapy for disabled people could be a valuable gateway for the residential facility of the future. In this work, we design and develop a SAR that can be used for cognitive therapy proposing music and game activities. The ...

Substantive Interpretation of Machine Learning Solutions by the Example of Determining the Activity of the Tuberculosis Process in Individuals with Minimal Tuberculosis Residual Changes.

In this article is described an application of various machine learning (ML) methods to obtain decision rules and its interpretation to a problem of r...

Inference Time of a CamemBERT Deep Learning Model for Sentiment Analysis of COVID Vaccines on Twitter.

In previous work, we implemented a deep learning model with CamemBERT and PyTorch, and built a microservices architecture using the TorchServe serving...

NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov.

Core outcome sets (COS) are necessary to ensure the systematic collection, metadata analysis and sharing the information across studies. However, deve...

The Pitfalls of Using Open Data to Develop Deep Learning Solutions for COVID-19 Detection in Chest X-Rays.

Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to h...

A Natural Language Processing Tool Offering Data Extraction for COVID-19 Related Information (DECOVRI).

A new natural language processing (NLP) application for COVID-19 related information extraction from clinical text notes is being developed as part of...

Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.

In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a centra...

Deep learning based on biologically interpretable genome representation predicts two types of human adaptation of SARS-CoV-2 variants.

Explosively emerging SARS-CoV-2 variants challenge current nomenclature schemes based on genetic diversity and biological significance. Genomic compos...

Do deep learning models make a difference in the identification of antimicrobial peptides?

In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the deve...

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit d...

deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host-pathogen protein-protein interactions.

Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases. With the ou...

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 algorithm...

Long COVID and cardiovascular disease: a learning health system approach.

Cardiovascular disease is both a risk factor and potential outcome of the direct, indirect and long-term effects of COVID-19. A recent analysis in >15...

Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.

BACKGROUND: Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnos...

Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes.

OBJECTIVE: This study aims to establish an informative dynamic prediction model of treatment outcomes using follow-up records of tuberculosis (TB) pat...

DNAcycP: a deep learning tool for DNA cyclizability prediction.

DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq ...

The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study.

BACKGROUND: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland reg...

Potential SARS-CoV-2 nonstructural proteins inhibitors: drugs repurposing with drug-target networks and deep learning.

BACKGROUND: In the current COVID-19 pandemic, with an absence of approved drugs and widely accessible vaccines, repurposing existing drugs is vital to...

Spatial resolution improved fluorescence lifetime imaging via deep learning.

We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from fluoresc...