Results 201 to 210 of about 91,735 (266)

Metabolomics-guided machine learning reveals diagnostic and mechanistic biomarkers in CHB with MASLD. [PDF]

PLoS One
Wang C, Chen Y, Xiao H, Cai J, Wang R, Zeng X, Lin M, Liu W, Chi X, Chen Q.   +9 more
europepmc   +1 more source

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

Machine Learning Analysis of Retrospective Data From 503 Hospitalized Older Patients With Type 2 Diabetes to Identify Factors Associated With Cognitive Impairment. [PDF]

Med Sci Monit
Yu M, Zhang J, Chen H, Li G.
europepmc   +1 more source

Proteomics of Nitrotyrosine: Integrating Mass Spectrometry and Immunodetection in Redox‐Driven Pathology

Mass Spectrometry Reviews, EarlyView.
ABSTRACT Nitrooxidative stress, driven by excess reactive nitrogen species like peroxynitrite, contributes to the pathogenesis of many chronic diseases. Among its molecular footprints, 3‐nitrotyrosine (3NT) has emerged as a biologically relevant marker of protein nitration.
Brîndușa Alina Petre
wiley   +1 more source

E2E: An R Package for Easy‐to‐Build Ensemble Models

Med Research, EarlyView.
ABSTRACT E2E (Easy to Ensemble) is an R package aiming to provide researchers with a simple, user‐friendly, and comprehensive ensemble learning framework to address the challenges of existing tools when dealing with imbalanced data or constructing complex models.
Shanjie Luan, Ximing Wang
wiley   +1 more source

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Med Research, EarlyView.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish Kumar Agrawal   +3 more
wiley   +1 more source

Leveraging topological indices and machine learning for advanced prediction of antidepressant drug properties. [PDF]

Sci Rep
Zhang G, Noureen S, Azam S, Abdalla MEM, Dafaalla ME, Aslam A, Tola KA.   +6 more
europepmc   +1 more source

Advancing Severe Plastic Deformation for Tubular Samples: Systematic Review and Machine Learning Optimization

MetalMat, EarlyView.
This review highlights recent advancements in Severe Plastic Deformation (SPD) techniques tailored for tubular samples. The mechanisms of processing are critically discussed with emphasis on microstructural control and the governing process parameters that enable refinement to ultrafine‐ and nano‐grained structures. Comparative insights into equivalent
Eman M. Zayed, Abdulrahman I. Alateyah, Waleed H. El‐Garaihy, Mahmoud Shaban, Majed Alinizzi, Nahed A. El‐Mahallawy   +5 more
wiley   +1 more source

Artificial intelligence and precision medicine: a pilot study predicting optimal ceftaroline dosage for pediatric patients. [PDF]

Front Artif Intell
Frasca M, Gazzaniga G, Graziosi A, De Nicolo V, De Giacomo C, Martinelli S, Senatore M, Romandini A, Moretti C, Pattarino GAC, Proto A, Danesi R, Scaglione F, Vago G, La Torre D, Pani A.   +15 more
europepmc   +1 more source

Designing High‐Entropy Alloys With Low Stacking Fault Energy Through Interpretable Machine Learning

Materials Genome Engineering Advances, EarlyView.
In this study, we developed an interpretable machine learning (ML) ensemble framework and, by integrating the VEC criterion with the proposed machine learning scoring parameter in the alloy composition screening process, successfully designed multiple CoCrFeNiMn‐based HEAs with TWIP/TRIP effects and without the BCC phase.
Shuai Nie, Yixuan He, Haoxiang Liu, Xudong Liu, Haifeng Wang, Ziqing He, Menghao Yang   +6 more
wiley   +1 more source