Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s10840-023-01669-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Improving the Reliability of Language Model-Predicted Structures as Docking Targets through Geometric Graph Learning.
J Med Chem
January 2025
Hangzhou Carbonsilicon AI Technology Company Limited, Hangzhou 310018, Zhejiang, China.
Applying artificial intelligence techniques to flexibly model the binding between the ligand and protein has attracted extensive interest in recent years, but their applicability remains improved. In this study, we have developed CarsiDock-Flex, a novel two-step flexible docking paradigm that generates binding poses directly from predicted structures. CarsiDock-Flex consists of an equivariant deep learning-based model termed CarsiInduce to refine ESMFold-predicted protein pockets with the induction of specific ligands and our existing CarsiDock algorithm to redock the ligand into the induced binding pockets.
View Article and Find Full Text PDFEnhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.
PLoS One
January 2025
Institute for Physical Activity and Nutrition, Deakin University, Melbourne, VIC, Australia.
Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators.
View Article and Find Full Text PDFConjunctival Bulbar Redness Extraction Pipeline for High-Resolution Ocular Surface Photography.
Transl Vis Sci Technol
January 2025
Institute of the Electrical and Biomedical Engineering, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tyrol, Austria.
Purpose: To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline.
Methods: Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity.
Background: With the approval of several anti-amyloid antibodies and a robust pipeline of new amyloid-based therapies, attention turns towards questions related to real-world clinical practice. Here we explore the impact of several biological pathways on the amyloid biomarker response of AD patients using a Quantitative Systems Pharmacology (QSP) approach with the ultimate objective to find measurable biomarkers for responder identification.
Method: Using a well-validated QSP biophysically realistic model of amyloid aggregation, we performed sensitivity analysis to identify key drivers of amyloid biomarkers both in a longitudinal observational context and after treatment with specific amyloid antibodies.
Background: The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method: We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high-dimensional neuroimaging data for AD progression, incorporating age and gender as covariates.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!