Results 51 to 60 of about 3,102,730 (292)

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

open access: yesMolecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

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

open access: yesFrontiers 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

Inversion of Bayesian networks

open access: yesInternational 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   +2 more
openaire   +4 more sources

Directed evolution of enzymes at the crossroads of tradition and innovation

open access: yesFEBS 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   +2 more
wiley   +1 more source

Additive Bayesian Networks

open access: yesJournal 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   +3 more
openaire   +3 more sources

Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks [PDF]

open access: yesMachine Learning, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nir Friedman, Daphne Koller
openaire   +1 more source

Structural and biochemical insights into the thermostable esterase Ta0887 from Thermoplasma acidophilum

open access: yesFEBS Open Bio, EarlyView.
In this study, a novel esterase from the thermoacidophilic archaeon Thermoplasma acidophilum was biochemically and structurally characterized. Our results demonstrate that Ta0887 is a highly thermostable esterase that preferentially hydrolyzes p‐nitrophenyl hexanoate and possesses an α‐helical cap domain that likely contributes to its substrate ...
Alejandro Delgado‐Rey   +4 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

open access: yesRedai 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   +3 more
doaj   +1 more source

Global Rather Than Vertical‐Selective Saccadic Abnormalities in Progressive Supranuclear Palsy

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To test whether vertical saccades are preferentially affected in Progressive Supranuclear Palsy (PSP). Methods PSP patients (n = 24) were compared to age‐matched controls (n = 94) and two degenerative groups (Alzheimer's disease, n = 20; Lewy body disease, n = 50).
Duy Duan Nguyen   +6 more
wiley   +1 more source

Bayesian network–response regression [PDF]

open access: yesBioinformatics, 2017
Abstract Motivation There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited.
Lu Wang 0015   +3 more
openaire   +4 more sources

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