Results 101 to 110 of about 5,008 (215)
Modular network construction using eQTL data: an analysis of computational costs and benefits
Frontiers in Genetics, 2014 Background: In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation and mRNA expression (also named as eQTL data), to discover genetic networks that are associated with a complex trait of interest.
Yen-Yi eHo +2 more
doaj +1 more source
Advanced Robotics Research, EarlyView.A sea turtle‐inspired amphibious robot uses simulation‐guided optimization to adapt its flipper coordination to different environments. Sensitivity analysis and multiobjective Bayesian optimization identify terrain‐dependent controllers that are validated on hardware.
Nnamdi C. Chikere, Yasemin Ozkan‐Aydin
wiley +1 more source
Consensus Formation and Change are Enhanced by Neutrality
Advanced Science, EarlyView.Neutral agents are shown to enhance both the formation and overturning of consensus in collective decision‐making. A general mathematical model and experiments with locusts and humans reveal that neutrality enables robust consensus via simple interactions and accelerates consensus change by reducing effective population size.
Andrei Sontag +3 more
wiley +1 more source
American universities in the Middle East: A student’s perspective
Cogent Education, 2018 Many American universities located in the Middle East try to offer the stamp of higher quality in education that the United States provides and delivers. These institutions are doing an incredible job of providing opportunity for youths of that region to
Linda Smail, Ginger Silvera
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Improved learning of Bayesian networks
CoRR, 2001 The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Bayesian Networks is ordered by DAG Markov model inclusion and it is natural to consider that a good search policy should take this into account.
Kocka, T., Castelo Valdueza, R.
openaire +4 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 +8 more
wiley +1 more source
Bayesian Neural Networks via MCMC: A Python-Based Tutorial
IEEE AccessBayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian ...
Rohitash Chandra, Joshua Simmons
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Causality, Propensity, and Bayesian Networks [PDF]
Synthese, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
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 +3 more
wiley +1 more source
Frontiers in Cellular and Infection Microbiology, 2016 Antibiotics have significant and long-lasting impacts on the intestinal microbiota and consequently reduce colonization resistance against Clostridium difficile infection (CDI). Standard therapy using antibiotics is associated with a high rate of disease
Mária Džunková +10 more
doaj +1 more source