Results 191 to 200 of about 104,957 (252)

University english teaching evaluation using artificial intelligence and data mining technology. [PDF]

Sci Rep
Huang Q, Li W, Mokhtar Bin Muhamad M, Nawi NRBC, Liu X.   +4 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

PMADS: an integrated database of curated and proteomics-inferred associations between protein post-translational modifications and drug sensitivity. [PDF]

Nucleic Acids Res
Zheng J, Chen S, Zhang Y, Zhang B, Qiao K, Xu J, Fan Y, Feng M, Feng Z, Wang Z, Wang H.   +10 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Explainable Transformer-Based Modelling for Pathogen-Oriented Food Safety Inspection Grade Prediction Using New York State Open Data. [PDF]

Foods
Sari OF, Bader-El-Den M, Ince V.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023. [PDF]

Sci Rep
Melaku MS, Baykemagn ND, Yohannes L, Zegeye AT.   +3 more
europepmc   +1 more source

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

Comparing the performance of narrow vs. broad search strategies when using machine learning-based software for title/abstract screening. [PDF]

J Med Libr Assoc
Swab M.
europepmc   +1 more source

Research on personalized distance education recommendation system based on deep learning. [PDF]

Sci Rep
Yang X.
europepmc   +1 more source