Results 161 to 170 of about 35,453 (267)

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

Enhanced sentiment analysis in tourism reviews via multimodal graph convolutional networks. [PDF]

PLoS One
Yang W, Xue Z, Fu G, Chen G, Yang C.
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

BrainGraphNet-ViT model: a hybrid model combining vision transformers and graph convolutional networks for brain tumor diagnosis. [PDF]

Sci Rep
Elhamzi W, Mokni R.
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

BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks. [PDF]

Quant Biol
Sartono A, Trilaksono BR, Damayanti S, Nugroho AS, Firdayani F.   +4 more
europepmc   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

Multimodal data-based graph convolutional networks for predicting outcomes in ovarian cancer receiving neoadjuvant chemotherapy. [PDF]

NPJ Precis Oncol
Zhang S, Liu Y, Liu Z, Li X, Wang G, Yang Z, Liu Y, Li M, Wang J, Zhang J, Chen B, Liu J, Zhang Y, Song J, Zhou X.   +14 more
europepmc   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

Research on traffic flow prediction of progressive graph convolutional networks based on spatio-temporal self-attention mechanism. [PDF]

Sci Rep
Liu C, Kou Y, Wang S, Xie Z, Su Y.
europepmc   +1 more source