HIV/AIDS

Ligand-Based Drug Discovery Leveraging State-of-the-Art Machine Learning Methodologies Exemplified by Cdr1 Inhibitor Prediction.

Artificial intelligence (AI) is revolutionizing drug discovery with unprecedented speed and efficiency. In computer-aided drug design, structure-based and ligand-based methodologies are the main driving forces for innovation. In cases where no experimental structure or high-confidence homology/AlphaFold-predicted model of the target is available in 3D, ligand-based strategies are generally prefera...

AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm.

Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical applications. To simulate real-world scenarios, this study proposed a cue-masked auditory attention paradigm to avoid information leakage before the exper...

Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.

Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Ne...

Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases.

Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobiliary and hepatic conditions. A key focus is differ...

Analysis and validation of hub genes for atherosclerosis and AIDS and immune infiltration characteristics based on bioinformatics and machine learning.

Atherosclerosis is the major cause of cardiovascular diseases worldwide, and AIDS linked with chronic inflammation and immune activation, increases at...

Spatial and temporal classification and prediction of aspen probability in boreal forests using machine learning algorithms.

Mapping and classifying the probability of occurrence of Populus tremula L. (aspen) in boreal forests is a complex task for sustainable forest managem...

Robot-assisted ultrasound probe calibration for image-guided interventions.

BACKGROUND: Trackable ultrasound probes facilitate ultrasound-guided procedures, allowing real-time fusion of augmented ultrasound images and live vid...

Unveiling drug-induced osteotoxicity: A machine learning approach and webserver.

Drug-induced osteotoxicity refers to the harmful effects certain pharmaceuticals have on the skeletal system, posing significant safety risks. These t...

Gold nanorods as multidimensional optical nanomaterials: machine learning-enhanced quantitative fingerprinting of proteins for diagnostic applications.

The rapid and precise quantification and identification of proteins as key diagnostic biomarkers hold significant promise in allergy testing, disease ...

Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.

The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney di...

Systematic review of infodemiology studies using artificial intelligence: social media posts on HIV preexposure prophylaxis.

OBJECTIVES: To explore how artificial intelligence (AI) can enhance infodemiology, which distributes and scans information in the electronic medium, t...

Role of eccentricity based topological descriptors to predict anti-HIV drugs attributes with supervised machine learning algorithms.

Chemical graphs are mathematical representations of molecular structures, where atoms are represented as vertices, while chemical bonds are depicted a...

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subt...

TCDE-Net: An unsupervised dual-encoder network for 3D brain medical image registration.

Medical image registration is a critical task in aligning medical images from different time points, modalities, or individuals, essential for accurat...

Artificial intelligence-assisted magnetic resonance lymphography for evaluation of micro- and macro-sentinel lymph node metastasis in breast cancer.

Contrast-enhanced magnetic resonance lymphography (CE-MRL) plays a crucial role in preoperative diagnostic for evaluating tumor metastatic sentinel ly...

Infection and Inflammation in Nuclear Medicine Imaging: The Role of Artificial Intelligence.

Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment lead to death, disabilities and impairment of the q...

Urban change detection: assessing biophysical drivers using machine learning and Google Earth Engine.

Urban areas are experiencing rapid transformations, driven by population growth, economic development, and policy changes. Understanding and monitorin...

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mortality. Predicting the survival of CRC patients not...

Data-driven cultural background fusion for environmental art image classification: Technical support of the dual Kernel squeeze and excitation network.

This study aims to explore a data-driven cultural background fusion method to improve the accuracy of environmental art image classification. A novel ...

Evaluating the performance of random forest, support vector machine, gradient tree boost, and CART for improved crop-type monitoring using greenest pixel composite in Google Earth Engine.

The development of machine learning algorithms, along with high-resolution satellite datasets, aids in improved agriculture monitoring and mapping. Ne...