Results 171 to 180 of about 39,124 (245)

HiGLDP: a hierarchical graph neural network for predicting lncRNA-disease associations through multi-omic integration. [PDF]

BMC Biol
Wang Y, Wang Z, Wang T, Hu J, You Z, Peng J.   +5 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

Improved Point Cloud Representation via a Learnable Sort-Mix-Attend Mechanism. [PDF]

Sensors (Basel)
Zhang Y, Wang X, Yi Z, Xu L.
europepmc   +1 more source

Semantic Embeddings of Chemical Elements for Enhanced Materials Inference and Discovery

Materials Genome Engineering Advances, EarlyView.
ElementBERT extracts semantic embeddings of chemical elements from 1.29 million alloy‐related abstracts, providing robust descriptors that improve prediction accuracy by up to 23% across titanium, high‐entropy, and shape memory alloys, with demonstrated generalization on alloy compositions reported in 2025.
Yunze Jia, Yuehui Xian, Yangyang Xu, Pengfei Dang, Xiangdong Ding, Jun Sun, Yumei Zhou, Dezhen Xue   +7 more
wiley   +1 more source

Machine Learning-Based Lung Cancer Classification Using Blood-Derived Microbial DNA: A Comparative Analysis of Taxonomic Profiling Strategies. [PDF]

Diagnostics (Basel)
Goh CJ, Park J, Kim Y, Park D, Kim J, Kwon SJ, Kim MJ, Lee MS.   +7 more
europepmc   +1 more source

Application of Machine Learning in Heat Treatment Process Design of Carburized Steel

Materials Genome Engineering Advances, EarlyView.
To accelerate heat treatment design, we constructed a closed‐loop machine learning strategy involving multi‐source datasets and feature screening. The optimized model accurately predicts hardness and friction coefficients, successfully guiding the process optimization for two typical carburized steels with high experimental consistency.
Di Jiang, Yihao Zheng, Chunyang Luo, Xuefei Wang, Chi Zhang, Zhaodong Wang   +5 more
wiley   +1 more source

Stable Longitudinal Screening of Latent Physiological Dysregulation from Psychometric Data Using Machine Learning. [PDF]

Bioengineering (Basel)
Alecu AA.
europepmc   +1 more source

Machine Learning‐Driven Predictive Modeling and Multi‐Objective Exploration of Oxaliplatin‐Loaded Nanocarriers for Enhanced Loading and Encapsulation Efficiency

Materials Genome Engineering Advances, EarlyView.
Integrating ML and multi‐objective optimization enabled efficient, accurate design of nanocarriers with optimized loading and encapsulation efficiencies. ABSTRACT Oxaliplatin (OXA), a critical third‐generation platinum chemotherapeutic, is significantly limited by suboptimal loading capacity and encapsulation efficiency in nanoparticle‐based delivery ...
Abbas Rahdar, Maryam Shirzad, Sonia Fathi‐Karkan, M. Ali Aboudzadeh   +3 more
wiley   +1 more source

Machine Learning-Based Depression Risk Prediction Models for Older Adults Analyzing From the Perspective of the Health Ecology Model: A Scoping Review. [PDF]

Neuropsychiatr Dis Treat
Yang J, Hao X, Sang X, Jiang E, Zhang X.
europepmc   +1 more source

Elastoplasticity Informed Kolmogorov–Arnold Networks Using Chebyshev Polynomials

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT Multilayer perceptron (MLP) networks are predominantly used to develop data‐driven constitutive models for granular materials. They offer a compelling alternative to traditional physics‐based constitutive models in predicting non‐linear responses of these materials, for example, elastoplasticity, under various loading conditions. To attain the
Farinaz Mostajeran, Salah A. Faroughi
wiley   +1 more source