Flu & URI

An Integrated Approach to Develop a Potent Vaccine Candidate Construct Against Prostate Cancer by Utilizing Machine Learning and Bioinformatics.

BACKGROUND: Prostate cancer is the most common malignancy among males. Prostaglandin G/H synthase (PGHS) is an essential enzyme in the synthesis of prostaglandins, and its activation has been linked to many malignancies, including colorectal cancer.

Examining the Influenza A Virus Sialic Acid Binding Preference Predictions of a Sequence-Based Convolutional Neural Network.

BACKGROUND: Though receptor binding specificity is well established as a contributor to host tropism and spillover potential of influenza A viruses, determining receptor binding preference of a specific virus still requires expensive and time-consuming laboratory analyses. In this study, we pilot a machine learning approach for prediction of binding preference.

Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through Machine Learning

The accurate prediction of B-cell epitopes is critical for guiding vaccine development against infectious diseases, including SARS and COVID-19. Thi...

Machine learning identification of maternal inflammatory response and histologic choroamnionitis from placental membrane whole slide images

The placenta forms a critical barrier to infection through pregnancy, labor and, delivery. Inflammatory processes in the placenta have short-term, a...

Automated detection of maxillary sinus opacifications compatible with sinusitis from CT images.

BACKGROUND: Sinusitis is a commonly encountered clinical condition that imposes a considerable burden on the healthcare systems. A significant number ...

Revolutionizing Personalized Cancer Vaccines with NEO: Novel Epitope Optimization Using an Aggregated Feed Forward and Recurrent Neural Network with LSTM Architecture

As cancer cases continue to rise, with a 2023 study from Zhejiang and Harvard predicting a 31 percent increase in cases and a 21 percent increase in...

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The COVID-19 pandemic is marked by the successive emergence of new SARS-CoV-2 variants, lineages, and sublineages that outcompete earlier strains, lar...

Towards reliable respiratory disease diagnosis based on cough sounds and vision transformers

Recent advancements in deep learning techniques have sparked performance boosts in various real-world applications including disease diagnosis based...

Analyzing factors of daily travel distances in Japan during the COVID-19 pandemic.

The global impact of the COVID-19 pandemic is widely recognized as a significant concern, with human flow playing a crucial role in its propagation. C...

Flusion: Integrating multiple data sources for accurate influenza predictions

Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the mo...

Exploring the Design of Collaborative Applications via the Lens of NDN Workspace

Metaverse applications desire to communicate with semantically identified objects among a diverse set of cyberspace entities, such as cameras for co...

SecureVAX: A Blockchain-Enabled Secure Vaccine Passport System

A vaccine passport serves as documentary proof, providing passport holders with greater freedom while roaming around during pandemics. It confirms v...

New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data

The stakeholders' needs in sentiment analysis for various issues, whether positive or negative, are speed and accuracy. One new challenge in sentime...

Towards Fluorescent-Tag-Less Viral Titration: Automated Estimation of Cell-Size Distribution and Infection Level from Phase-Contrast Microscopy Using Deep Learning and Transfer Learning.

Automated detection of infected insect cells is one of the crucial tasks in the field of recombinant protein production and vaccine development. The m...

Audio Cough Analysis by Parametric Modelling of Weighted Spectrograms to Interpret the Output of Convolutional Neural Networks.

This study explores the feasibility of employing eXplainable Artificial Intelligence (XAI) methodologies for the analysis of cough patterns in respira...

Cough Sound Based Deep Learning Models for Diagnosis of COVID-19 Using Statistical Features and Time-Frequency Spectrum.

This paper presents a deep learning model that can classify COVID-19 patients through cough sounds. The cough sound data were selected from the Cambri...

Cough Classification of Unknown Emerging Respiratory Disease with Federated Learning.

Artificial intelligence offers great potential to address the need for rapid diagnostic testing in pandemic scenarios. Concerns about security and pri...

Cough-DL: A Deep Learning Model for Ear-Worn Cough Detection.

Cough serves as a crucial bio-marker for evaluation and monitoring of pulmonary conditions. With growing interest towards automatic cough detection sy...

How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance

Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Weara...

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen p...