Results 161 to 170 of about 7,255,480 (346)
Empirical evaluation of scoring functions for Bayesian network model selection
BMC Bioinformatics, 2012 In this work, we empirically evaluate the capability of various scoring functions of Bayesian networks for recovering true underlying structures. Similar investigations have been carried out before, but they typically relied on approximate learning ...
Liu Zhifa, Malone Brandon, Yuan Changhe
doaj +1 more source
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
Advanced Science, EarlyView.By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin +12 more
wiley +1 more source
Advanced Science, EarlyView.Single‐cell and spatial profiling of 110 human thoracic aortic samples reveals a stromal–immune circuit driving aortic dissection. An elastin‐rich fibroblast subset is depleted with age and markedly reduced in disease, weakening aortic wall integrity.
Jing Tao +25 more
wiley +1 more source
The use of Bayesian networks to facilitate implementation of water demand management strategies [PDF]
, 2008 Bayesian networks have received increasing recognition in recent years as a potentially effective tool in supporting water management decisions. Despite a number of reports of their use, no formal evaluation of the effectiveness of Bayesian networks ...
Inman, David
core
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 +5 more
wiley +1 more source
Bayesian non-linear matching of pairwise microarray gene expressions [PDF]
, 2008 In this paper, we present a Bayesian non-linear model to analyze matching pairs of microarray expression data. This model generalizes, in terms of neural networks, standard linear matching models. As a practical application, we analyze data of patients
Marín Díazaraque, Juan Miguel +2 more
core
A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces
Advanced Science, EarlyView.A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren +5 more
wiley +1 more source
Advanced Science, EarlyView.An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen +7 more
wiley +1 more source
Probabilistic inference in Bayesian neural networks [PDF]
Despite widespread applicability and the dominant role in machine learning, neural networks remain highly non-transparent and are often regarded as black boxes due to the lack of human-understandable interpretations. Conventional deep models tend to be
Sheinkman, Alisa
core +1 more source