FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893116636> ?p ?o ?g. }

• W2893116636 abstract "Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA)." @default.
• W2893116636 created "2018-10-05" @default.
• W2893116636 creator A5017328816 @default.
• W2893116636 creator A5021432602 @default.
• W2893116636 creator A5072545849 @default.
• W2893116636 date "2020-03-01" @default.
• W2893116636 modified "2023-09-26" @default.
• W2893116636 title "Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior" @default.
• W2893116636 cites W1484418241 @default.
• W2893116636 cites W1813571362 @default.
• W2893116636 cites W1915506416 @default.
• W2893116636 cites W1923906081 @default.
• W2893116636 cites W1965125844 @default.
• W2893116636 cites W1978486653 @default.
• W2893116636 cites W1982652137 @default.
• W2893116636 cites W1982992776 @default.
• W2893116636 cites W1988899773 @default.
• W2893116636 cites W1990125482 @default.
• W2893116636 cites W1990885553 @default.
• W2893116636 cites W1991086178 @default.
• W2893116636 cites W2002055996 @default.
• W2893116636 cites W2004409905 @default.
• W2893116636 cites W2005129098 @default.
• W2893116636 cites W2006217430 @default.
• W2893116636 cites W2007069447 @default.
• W2893116636 cites W2008620264 @default.
• W2893116636 cites W2008703230 @default.
• W2893116636 cites W2016450639 @default.
• W2893116636 cites W2020925091 @default.
• W2893116636 cites W2021896712 @default.
• W2893116636 cites W2023096047 @default.
• W2893116636 cites W2026437259 @default.
• W2893116636 cites W2029164135 @default.
• W2893116636 cites W2030911724 @default.
• W2893116636 cites W2031891213 @default.
• W2893116636 cites W2035983696 @default.
• W2893116636 cites W2036183522 @default.
• W2893116636 cites W2040097804 @default.
• W2893116636 cites W2044702943 @default.
• W2893116636 cites W2045688706 @default.
• W2893116636 cites W2047940964 @default.
• W2893116636 cites W2056440286 @default.
• W2893116636 cites W2062533676 @default.
• W2893116636 cites W2074682976 @default.
• W2893116636 cites W2076283550 @default.
• W2893116636 cites W2092646816 @default.
• W2893116636 cites W2096514143 @default.
• W2893116636 cites W2099921231 @default.
• W2893116636 cites W2104798373 @default.
• W2893116636 cites W2111898680 @default.
• W2893116636 cites W2116063398 @default.
• W2893116636 cites W2119615097 @default.
• W2893116636 cites W2122825543 @default.
• W2893116636 cites W2126797928 @default.
• W2893116636 cites W2133903921 @default.
• W2893116636 cites W2135046866 @default.
• W2893116636 cites W2138255346 @default.
• W2893116636 cites W2140883620 @default.
• W2893116636 cites W2141619730 @default.
• W2893116636 cites W2146899967 @default.
• W2893116636 cites W2150149003 @default.
• W2893116636 cites W2160182981 @default.
• W2893116636 cites W2293257570 @default.
• W2893116636 cites W2345396394 @default.
• W2893116636 cites W2419268404 @default.
• W2893116636 cites W2442036653 @default.
• W2893116636 cites W2525638059 @default.
• W2893116636 cites W2548769923 @default.
• W2893116636 cites W2589100666 @default.
• W2893116636 cites W2591484772 @default.
• W2893116636 cites W2707916185 @default.
• W2893116636 cites W2765396870 @default.
• W2893116636 cites W2767057706 @default.
• W2893116636 cites W2779151065 @default.
• W2893116636 cites W2789666856 @default.
• W2893116636 cites W2791992901 @default.
• W2893116636 cites W2963110785 @default.
• W2893116636 cites W2963126261 @default.
• W2893116636 cites W2963218881 @default.
• W2893116636 cites W3100858558 @default.
• W2893116636 cites W3101256146 @default.
• W2893116636 cites W3102998992 @default.
• W2893116636 cites W3104258896 @default.
• W2893116636 doi "https://doi.org/10.1214/18-ba1142" @default.
• W2893116636 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7428197" @default.
• W2893116636 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32802246" @default.
• W2893116636 hasPublicationYear "2020" @default.
• W2893116636 type Work @default.
• W2893116636 sameAs 2893116636 @default.
• W2893116636 citedByCount "10" @default.
• W2893116636 countsByYear W28931166362020 @default.
• W2893116636 countsByYear W28931166362021 @default.
• W2893116636 countsByYear W28931166362022 @default.
• W2893116636 countsByYear W28931166362023 @default.
• W2893116636 crossrefType "journal-article" @default.
• W2893116636 hasAuthorship W2893116636A5017328816 @default.
• W2893116636 hasAuthorship W2893116636A5021432602 @default.
• W2893116636 hasAuthorship W2893116636A5072545849 @default.
• W2893116636 hasBestOaLocation W28931166361 @default.
• W2893116636 hasConcept C115178988 @default.

• next