COVID-19

MSA-MaxNet: Multi-Scale Attention Enhanced Multi-Axis Vision Transformer Network for Medical Image Segmentation.

Convolutional neural networks (CNNs) are well established in handling local features in visual tasks; yet, they falter in managing complex spatial relationships and long-range dependencies that are crucial for medical image segmentation, particularly in identifying pathological changes. While vision transformer (ViT) excels in addressing long-range dependencies, their ability to leverage local fea...

[Discussion about Testing Scheme of Intelligent Medical Devices].

Intelligent medical devices are flourishing with the deep integration of modern information and artificial intelligence technologies into healthcare. Testing is an important means of performance evaluation and quality control for intelligent medical devices. Compared with traditional medical devices, the testing methods and technologies of intelligent medical devices are still immature, and need a...

How chromatin interactions shed light on interpreting non-coding genomic variants: opportunities and future direc-tions

Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent ...

MADE: Graph Backdoor Defense with Masked Unlearning

Graph Neural Networks (GNNs) have garnered significant attention from researchers due to their outstanding performance in handling graph-related tas...

DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction

Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. ...

Gradient-Guided Parameter Mask for Multi-Scenario Image Restoration Under Adverse Weather

Removing adverse weather conditions such as rain, raindrop, and snow from images is critical for various real-world applications, including autonomo...

A Decision Support System for daily scheduling and routing of home healthcare workers with a lunch break consideration

This study examines a home healthcare scheduling and routing problem (HHSRP) with a lunch break requirement. This problem especially consists of lun...

ML-Based Framework to Predict the Severity of the Symptomatology in Patients with Post-Acute COVID-19 Syndrome.

The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of ...

LOGOWheat: deep learning-based prediction of regulatory effects for noncoding variants in wheats.

Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in ...

Primary care research on hypertension: A bibliometric analysis using machine-learning.

Hypertension is one of the most important chronic diseases worldwide. Hypertension is a critical condition encountered frequently in daily life, formi...

Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.

Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phe...

Deep learning-based design and experimental validation of a medicine-like human antibody library.

Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, th...

S$^2$ALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning

Antibodies safeguard our health through their precise and potent binding to specific antigens, demonstrating promising therapeutic efficacy in the t...

Exploring the Use of AI for Enhanced Accessibility Testing of Web Solutions.

Artificial Intelligence (AI) holds significant potential for enhancing accessibility and user experience across digital products and services. However...

Leveraging genomic deep learning models for non-coding variant effect prediction

The majority of genetic variants identified in genome-wide association studies of complex traits are non-coding, and characterizing their function r...

EEG Spectral Analysis in Gray Zone Between Healthy and Insomnia

This study investigates the sleep characteristics and brain activity of individuals in the gray zone of insomnia, a population that experiences slee...

MANTIS: A Mixed-Signal Near-Sensor Convolutional Imager SoC Using Charge-Domain 4b-Weighted 5-to-84-TOPS/W MAC Operations for Feature Extraction and Region-of-Interest Detection

Recent advances in artificial intelligence have prompted the search for enhanced algorithms and hardware to support the deployment of machine learni...

Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows

Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the opti...

Integrating Large Language Models for Genetic Variant Classification

The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics an...

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

Language models for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicin...