Results 181 to 190 of about 20,022 (282)

Causal Graphical Models and Their Applications. [PDF]

Entropy (Basel)
Sucar LE, Danks D.
europepmc   +1 more source

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

Bayesian Networks and Causal Discovery. [PDF]

Entropy (Basel)
Gao X, Wang Z.
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

European Workshop on Probabilistic Graphical Models, 17–19 September

The Reasoner, 2008
Manfred Jaeger, Thomas D. Nielsen
doaj  

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

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

Causal interventions in bond multi-dealer-to-client platforms. [PDF]

PLoS One
Marín Martínez P, Ardanza-Trevijano S, Sabio J.   +2 more
europepmc   +1 more source

G6PC Downregulation Promotes Renal Calcium Oxalate Stone Formation via Lactate‐Induced SNAIL1 K206 Lactylation and Epithelial‐Mesenchymal Transition

Advanced Science, EarlyView.
In renal calcium oxalate stone formation, G6PC downregulation leads to lactate accumulation. This lactate mediates CBP/p300‐dependent lactylation of SNAIL1 at K206, promoting its nuclear translocation. Nuclear SNAIL1 activates the TGF‐β/SMAD3 pathway, driving epithelial‐mesenchymal transition and fibrosis, which ultimately facilitates crystal ...
Kai Liu, Boming Zhang, Ruixin Fan, Chen Duan, Yangjun Zhang, Huahui Wu, Xiangyang Yao, Xiongmin Mao, Bo Li, Youmiao Zeng, Zhenzhen Xu, Yang Wang, Mengcheng Luo, Xinyu Xu, Ninghan Feng, Heng Li, Hua Xu   +16 more
wiley   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source