Infectious Disease

Leveraging Vision Transformers for Enhanced Accuracy in Pneumonia Detection from Medical Imaging Data.

Medical image analysis has witnessed a paradigm shift with the advent of artificial intelligence, particularly the application of Vision Transformers (ViTs). In this study, we leverage the unique attention mechanisms of ViTs to enhance the representation learning of pneumonia-related patterns in medical images. We propose a novel framework that integrates ViTs into the diagnostic pipeline, aiming ...

Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.

MOTIVATION: World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple an...

Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques

Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is cru...

A compact model of Escherichia coli core and biosynthetic metabolism

Metabolic models condense biochemical knowledge about organisms in a structured and standardised way. As large-scale network reconstructions are rea...

Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks

Tuberculosis persists as a global health crisis, especially in resource-limited populations and remote regions, with more than 10 million individual...

PathoLM: Identifying pathogenicity from the DNA sequence through the Genome Foundation Model

Pathogen identification is pivotal in diagnosing, treating, and preventing diseases, crucial for controlling infections and safeguarding public heal...

skandiver: a divergence-based analysis tool for identifying intercellular mobile genetic elements

Mobile genetic elements (MGEs) are as ubiquitous in nature as they are varied in type, ranging from viral insertions to transposons to incorporated ...

Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination

The COVID-19 pandemic exposed significant weaknesses in the healthcare information system. The overwhelming volume of misinformation on social media...

Research on Deep Learning Model of Feature Extraction Based on Convolutional Neural Network

Neural networks with relatively shallow layers and simple structures may have limited ability in accurately identifying pneumonia. In addition, deep...

PANDORA: Deep graph learning based COVID-19 infection risk level forecasting

COVID-19 as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. Policymakers and all elemen...

Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks

Neural networks that can capture key principles underlying brain computation offer exciting new opportunities for developing artificial intelligence...

Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases.

COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbre...

Results From a Pilot Study of an Automated Directly Observed Therapy Intervention Using Artificial Intelligence With Conditional Economic Incentives Among Young Adults With HIV.

BACKGROUND: Despite improvements in antiretroviral therapy (ART) availability, suboptimal adherence is common among youth with HIV (YWH) and can incre...

Image-based recognition of parasitoid wasps using advanced neural networks.

Hymenoptera has some of the highest diversity and number of individuals among insects. Many of these species potentially play key roles as food source...

A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models

Antibodies are crucial proteins produced by the immune system to eliminate harmful foreign substances and have become pivotal therapeutic agents for...

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences abo...

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...

TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation.

Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for i...

ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers.

Accurate understanding of the biological functions of enzymes is vital for various tasks in both pathologies and industrial biotechnology. However, th...

Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protei...