This article proposes a Bayesian computing algorithm to infer Gaussian directed acyclic graphs (DAG's). It has the ability of escaping local modes and maintaining adequate computing speed compared to existing methods. Simulations demonstrated that the proposed algorithm has low false positives and false negatives in comparison to an algorithm applied to DAG's. We applied the algorithm to an epigenetic data set to infer DAG's for smokers and non-smokers.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6433420 | PMC |
| http://dx.doi.org/10.1080/03610918.2016.1143103 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use.
IJCAI (U S)
August 2024
Department of Computer Science, Harvard University.
The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an online reinforcement learning (RL) algorithm called reBandit which will be utilized in a mobile health study to deliver personalized mobile health interventions aimed at reducing cannabis use among EAs.
View Article and Find Full Text PDFNeural mass modeling for the masses: Democratizing access to whole-brain biophysical modeling with FastDMF.
Netw Neurosci
December 2024
Institucio Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain.
Different whole-brain computational models have been recently developed to investigate hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is particularly attractive, combining a biophysically realistic model that is scaled up via a mean-field approach and multimodal imaging data. However, an important barrier to the widespread usage of the DMF model is that current implementations are computationally expensive, supporting only simulations on brain parcellations that consider less than 100 brain regions.
View Article and Find Full Text PDFExploiting full-duplex opportunities in WLANs via a reinforcement learning-based medium access control protocol.
Sci Rep
December 2024
National University of Defense Technology, Changsha, Hunan, China.
In-band full-duplex communication has the potential to double the wireless channel capacity. However, how to efficiently transform the full-duplex gain at the physical layer into network throughput improvement is still a challenge, especially in dynamic communication environments. This paper presents a reinforcement learning-based full-duplex (RLFD) medium access control (MAC) protocol for wireless local-area networks (WLANs) with full-duplex access points.
View Article and Find Full Text PDFBayesian semiparametric inference in longitudinal metabolomics data.
Sci Rep
December 2024
Department of Statistical Science, Duke University, Durham, 27708-0251, USA.
The article is motivated by an application to the EarlyBird cohort study aiming to explore how anthropometrics and clinical and metabolic processes are associated with obesity and glucose control during childhood. There is interest in inferring the relationship between dynamically changing and high-dimensional metabolites and a longitudinal response. Important aspects of the analysis include the selection of the important set of metabolites and the accommodation of missing data in both response and covariate values.
View Article and Find Full Text PDFProbabilistic prediction of phosphate ion adsorption onto biochar materials using a large dataset and online deployment.
Chemosphere
December 2024
Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA. Electronic address:
Phosphate (PO(III)) contamination in water bodies poses significant environmental challenges, necessitating efficient and accurate methods to predict and optimize its removal. The current study addresses this issue by predicting the adsorption capacity of PO(III) ions onto biochar-based materials using five probabilistic machine learning models: eXtreme Gradient Boosting LSS (XGBoostLSS), Natural Gradient Boosting, Bayesian Neural Networks (NN), Probabilistic NN, and Monte-Carlo Dropout NN. Utilizing a dataset of 2952 data points with 16 inputs, XGBoostLSS demonstrated the highest R (0.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!