Results 221 to 230 of about 30,503 (277)

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning. [PDF]

Adv Sci (Weinh)
Yu Z, Yang Y, Chen X, Wong KC, Zhang Z, Zhao Y, Li X.   +6 more
europepmc   +1 more source

Single‐Crystal PZT‐Driven Organic Piezo‐Phototronic Adaptive Transistors Toward Advanced Spatiotemporal Visual Computing

Advanced Science, EarlyView.
Here, we propose a single‐crystal PZT‐based piezo‐phototronic organic adaptive memory transistor (OAMT), achieving a record memory window capacity factor (γ) of 0.87 at a low SS of 200 mV/decade via efficient multi‐field control. The device achieves a high recognition accuracy ∼ 90% in neuromorphic simulations, demonstrates robust fault tolerance under
Chenhao Xu, Xingyu Cao, Zewen Li, Shifu Xiong, Yongxu Hu, Zhongwu Wang, Yujie Yuan, Lei Zheng, Wenping Hu   +8 more
wiley   +1 more source

Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning. [PDF]

Comput Struct Biotechnol J
Gui Y, Li C, Xu Y.
europepmc   +1 more source

Bacterial Outer Membrane Vesicles in Potentiating Cancer Vaccines: Progress and Prospects

Advanced Science, EarlyView.
Bacterial outer membrane vesicles (OMVs) have emerged as versatile platforms for cancer vaccine development owing to their intrinsic immunostimulatory properties and high engineering flexibility. This review summarizes OMV biology, immune mechanisms, and engineering strategies that enhance vaccine efficacy, discusses key translational challenges, and ...
Jiabeini Zhang, Tianyu Shao, Fengjiang Qu, Caiyun Bai, Sijie Li, Pin Gao, Wenna Li, Keman Cheng, Xiao Zhao, Zhimin Fan   +9 more
wiley   +1 more source

SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning. [PDF]

Brief Bioinform
Fan Y, Zhang C, Hu X, Huang Z, Xue J, Deng L.   +5 more
europepmc   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Graph contrastive learning of subcellular-resolution spatial transcriptomics improves cell type annotation and reveals critical molecular pathways. [PDF]

Brief Bioinform
Lu Q, Ding J, Li L, Chang Y.
europepmc   +1 more source

Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model

Advanced Science, EarlyView.
Understanding protein sequence–function relationships remains challenging due to poorly defined motifs and limited residue‐level annotations. An annotation‐agnostic framework is introduced that segments protein sequences into “protein words” using attention patterns from protein language models.
Hedi Chen, Jingrui Zhong, Xiaochun Zhang, Jingke Chen, Lin Guo, Xiaoliang Xiong, Xiaonan Zhang, Xiangyu Liu, Bailong Xiao, Boxue Tian   +9 more
wiley   +1 more source

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks. [PDF]

Brief Bioinform
Si Y, Huang Z, Fang Z, Yuan Z, Huang Z, Li Y, Wei Y, Wu F, Yao YF.   +8 more
europepmc   +1 more source