Results 261 to 270 of about 229,000 (311)

Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation

Advanced Science, EarlyView.
This study presents a scalable all‐electrical method for precise neuronal‐circuit reconfiguration based on high‐density microelectrode arrays. By employing biologically inspired plasticity rules, targeted connectivity changes were successfully induced and quantified across diverse neuronal preparations.
Sreedhar S. Kumar, Yannaël Bossard, Rachel Sava, Tobias Gänswein, Lorenca Sadiraj, Jean‐Samuel Dupré, Manuel Schröter, J. Gray Camp, Fernando Cardes, Julian Bartram, Andreas Hierlemann   +10 more
wiley   +1 more source

Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference. [PDF]

Brief Bioinform
He W, Zhang R, Zhu Y, Zhou H, Zuo Y, Bai Y, Yang L, Guo F.   +7 more
europepmc   +1 more source

SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi‐Omics Using Multimodal Mixture of Disentangled Experts

Advanced Science, EarlyView.
SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng, Xinlei Huang, Xiang Zhou, Dian Meng, Ziyue Qiao, Zhiyuan Yuan, Lixin Cheng   +6 more
wiley   +1 more source

Dynamic graph learning framework based seasonal and trend decomposition approach for potato crop evapotranspiration prediction. [PDF]

Sci Rep
Cheema SJ, Diykh M, Ali M, Farooque AA, Malekian R, Saleem SR, Galagedara LW, Sadiq R, Randhawa GS, Zaman QU.   +9 more
europepmc   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

AEPMA: peptide-microbe association prediction based on autoevolutionary heterogeneous graph learning. [PDF]

Brief Bioinform
Hu Z, Pan L, Zhang D, Bin Y, Su Y.
europepmc   +1 more source

A Portable and Dual‐Button Microneedle Device Enables Intelligent Multimodal Laser Sensing

Advanced Science, EarlyView.
A portable and dual‐button microneedle device enables rapid interstitial fluid sampling. Coupled with multimodal laser sensing and AI‐assisted data processing, the platform enables simultaneous molecular and elemental analysis for minimally invasive and multiplexed health assessment toward point‐of‐care diagnostics.
Yuanchao Liu, Xiujuan Hu, Shengqun Shi, Bowen Li, Zhixing Ge, Yunchen Long, Chaochao Sun, Annan Chen, Bingbing Gao, Lianbo Guo, Condon Lau, Wei Luo, Chwee Teck Lim   +12 more
wiley   +1 more source

Adaptive Graph Learning with Multimodal Fusion for Emotion Recognition in Conversation. [PDF]

Biomimetics (Basel)
Liu J, Li J, Dong J, Mo Z, Liu N, Li Q, Yuan Y.   +6 more
europepmc   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

Advanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source