Results 101 to 110 of about 234,090 (282)

Functional Approximations to Likelihoods/Posterior Densities: A Neural Network Approach to Efficient Sampling [PDF]

The performance of Monte Carlo integration methods like importance-sampling or Markov-Chain Monte-Carlo procedures depends greatly on the choice of the importance- or candidate-density.
Johan F. Kaashoek, Lennart F. Hoogerheide   +1 more
core  

Multi‐Tissue Genetic Regulation of RNA Editing in Pigs

Advanced Science, EarlyView.
This study presents the first multi‐tissue map of RNA editing and its genetic regulation in pigs. By integrating RNA editing profiles, edQTL mapping, GWAS, and cross‐species comparisons, this work establishes RNA editing as a distinct regulatory layer linking genetic variation to complex traits, highlighting its functional and evolutionary significance.
Xiangchun Pan, Wentao Gong, Xiaodian Cai, Jinyan Teng, Jiali Cai, Haonan Zeng, Wondossen Ayalew, Qingpeng Shen, Zhanming Zhong, Yifei Wang, Wenjing Zhang, Yuhan Tian, Dantong Xu, Yahui Gao, Hongwei Yin, Yuebo Zhang, Jiahui Hou, Tianru Zhou, Jiaqi Li, Lingzhao Fang, Xiaolong Yuan, Zhe Zhang   +21 more
wiley   +1 more source

Nanozymes Integrated Biochips Toward Smart Detection System

Advanced Science, EarlyView.
This review systematically outlines the integration of nanozymes, biochips, and artificial intelligence (AI) for intelligent biosensing. It details how their convergence enhances signal amplification, enables portable detection, and improves data interpretation.
Dongyu Chen, Wang Zheng, Zhihui Zhang, Shenping Yu, Xinxin Hang, Han Wu, Xiao‐Wei Xiang, Wei Mu, Yanli Jiao, Zaizai Dong, Lingqian Chang   +10 more
wiley   +1 more source

A bayesian-driven feedforward neural network model for Kafka cluster latency forecasting

Радіоелектронні і комп'ютерні системи
The subject matter of this article is the process of designing the architecture of a Feedforward neural network model based on the discrete Bayesian Network and a new method for setting the initial weights that connect neurons across layers.
Olha Solovei, Tetiana Honcharenko
doaj   +1 more source

Restricted Bayesian Neural Network

Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions. Additionally, they contend with various challenges, including the need for substantial storage space in large networks, issues of overfitting, underfitting, vanishing ...
Ganguly, Sourav, Bhattacharjee, Saprativa   +1 more
openaire   +2 more sources

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

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

A Design Methodology for Fault-Tolerant Neuromorphic Computing Using Bayesian Neural Network. [PDF]

Micromachines (Basel), 2023
Gao D, Xie X, Wei D.
europepmc   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Inverse solution of process parameters in gear grinding using hierarchical bayesian physics informed neural network (HBPINN)

Scientific Reports
Accurate inverse solution of process parameters by surface roughness is crucial for precision gear grinding processes. When inversely solving process parameters, model parameters are typically obtained by fitting experimental data.
Qi Zhang, Qiang Zhang, Yongsheng Zhao, Yanming Liu, Zhi Wang, Yali Ma   +5 more
doaj   +1 more source