Results 41 to 50 of about 399,768 (265)

Solving inference problems of Bayesian networks by probabilistic computing

AIP Advances, 2023
Recently, probabilistic computing approach has shown its broad application in problems ranging from combinatorial optimizations and machine learning to quantum simulation where a randomly fluctuating bit called p-bit constitutes a basic building block ...
Seokmin Hong
doaj   +1 more source

Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks

Machines, 2023
In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in
Madeleine Martinsen, Amare Desalegn Fentaye, Erik Dahlquist, Yuanye Zhou   +3 more
doaj   +1 more source

GENERATIONS IN BAYESIAN NETWORKS

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2019
This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents ...
Alexander Litvinenko, Natalya Litvinenko, Orken Mamyrbayev, Assem Shayakhmetova   +3 more
doaj   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Inversion of Bayesian networks

International Journal of Approximate Reasoning
Variational autoencoders and Helmholtz machines use a recognition network (encoder) to approximate the posterior distribution of a generative model (decoder). In this paper we study the necessary and sufficient properties of a recognition network so that it can model the true posterior distribution exactly.
Jesse van Oostrum, Peter van Hintum, Nihat Ay   +2 more
openaire   +3 more sources

Molecular dynamics simulations of positively selected codons in FcγRI reveal novel biochemical binding properties

FEBS Open Bio, EarlyView.
Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young, Omar Arias‐Gaguancela, Fiona Gibson, Morgan Grainger, Jodie Score, Amit Nathubhai, Mauricio Cafiero, Lee Richard Machado   +7 more
wiley   +1 more source

Multi-Scenario Simulation of Traditional Village Industrial System Risk: A Case Study of 148 Traditional Villages in She Xian, Huangshan

Redai dili, 2023
Owing to the effects of rural tourism and urbanization, the frequent participation of external market activities in traditional villages has increased the sensitivity and fragility of villages.
Chu Jinlong, Teng Lu, Li Jiulin, Yang Xuanxuan   +3 more
doaj   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Layer wise Scaled Gaussian Priors for Markov Chain Monte Carlo Sampled deep Bayesian neural networks

Frontiers in Artificial Intelligence
Previous work has demonstrated that initialization is very important for both fitting a neural network by gradient descent methods, as well as for Variational inference of Bayesian neural networks.
Devesh Jawla, John Kelleher
doaj   +1 more source

Additive Bayesian Networks

Journal of Open Source Software
The R package abn is a comprehensive tool for Bayesian Network (BN) analysis, a form of probabilistic graphical model. BNs are a type of statistical model that leverages the principles of Bayesian statistics and graph theory to provide a framework for representing complex multivariate data.
Delucchi, Matteo, Liechti, Jonas I, Spinner, Georg R, Furrer, Reinhard   +3 more
openaire   +2 more sources