Results 101 to 110 of about 207,453 (329)

Learning the structure of gene regulatory networks from time series gene expression data

BMC Genomics, 2011
Background Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on structure learning algorithm.
Li Haoni, Wang Nan, Gong Ping, Perkins Edward J, Zhang Chaoyang   +4 more
doaj   +1 more source

Automated Dynamic Bayesian Networks for Predicting Acute Kidney Injury Before Onset [PDF]

, 2023
David J. Gordon, Panayiotis Petousis, Anders O. Garlid, Keith C. Norris, Katherine R. Tuttle, Susanne B. Nicholas, Alex Bui   +6 more
openalex   +1 more source

Spintronic Bayesian Hardware Driven by Stochastic Magnetic Domain Wall Dynamics

Advanced Science, EarlyView.
Magnetic Probabilistic Computing (MPC) utilizes intrinsic stochastic dynamics in domain walls to establish a hardware foundation for uncertainty‐aware artificial intelligence. Thermally driven domain‐wall fluctuations, voltage‐controlled magnetic anisotropy, and TMR readout enable fully electrical, tunable probabilistic inference.
Tianyi Wang, Bingqian Dai, Shijie Xu, Kin Wong, Yaochen Li, Yang Cheng, Qingyuan Shu, Haoran He, Puyang Huang, Hanshen Huang, Xixiang Zhang, Kang L. Wang   +11 more
wiley   +1 more source

Dynamic Latent Space Model With Position Clusters and Its Application in International Trade Network

Discrete Dynamics in Nature and Society
The dynamic latent space model is widely used in analysing network data. It can provide useful visualization and interpretation of networks, as well as represent the inherent reciprocity and transitivity.
Jiajia Wang
doaj   +1 more source

Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox [PDF]

, 2012
The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better
Kenyon, Andrew, Plumley, Charles Edward, Quail, Francis, Wilson, Graeme, Zitrou, Athena   +4 more
core  

SpatialESD: Spatial Ensemble Domain Detection in Spatial Transcriptomics

Advanced Science, EarlyView.
ABSTRACT Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial
Hongyan Cao, Gaiqin Liu, Jingyi Xia, Runle Chen, Tong Wang, Xiaoling Yang, Ruiling Fang, Yanhong Luo, Ping Zeng, Hongmei Yu, Yanbo Zhang, Yuehua Cui   +11 more
wiley   +1 more source

Natural Variation of NAR5 Determines Nitrogenase Activity and the Yield in Soybean

Advanced Science, EarlyView.
This study identified NAR5, a gene encoding a subtilisin‐like protease, that regulates nitrogenase activity in soybean nodules. Overexpressing NAR5 delayed nodule senescence, enhancing nitrogenase activity, yield, and low‐nitrogen tolerance. The elite haplotype NAR5HapI‐1 linked to superior nitrogenase activity and greater seed weight has been ...
Chao Ma, Hanyu Zhao, Haojie Feng, Xulun Dong, Lin Chen, Mingliang Yang, Runyi Chen, Chengjun Lei, Chunyan Liu, Qingshan Chen, Dawei Xin, Jinhui Wang   +11 more
wiley   +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

Inferring dynamic genetic networks with low order independencies

, 2008
In this paper, we propose a novel inference method for dynamic genetic networks which makes it possible to face with a number of time measurements n much smaller than the number of genes p.
Lèbre, Sophie
core   +8 more sources

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source