FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3023843563> ?p ?o ?g. }

• W3023843563 endingPage "2723" @default.
• W3023843563 startingPage "2714" @default.
• W3023843563 abstract "Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. In this paper, we propose a novel model in predicting EMT based on multidimensional scaling (MDS) strategies and integrating entropy and random matrix detection strategies to determine the optimal reduced number of dimension in low dimensional space. We verified our proposed model with the gene expression data for EMT samples of breast cancer and the experimental results demonstrated the superiority over state-of-the-art clustering methods. Furthermore, we developed a novel feature extraction method for selecting the significant genes and predicting the tumor metastasis. The source code is available at https://github.com/yushanqiu/yushan.qiu-szu.edu.cn." @default.
• W3023843563 created "2020-05-13" @default.
• W3023843563 creator A5055139178 @default.
• W3023843563 creator A5079234443 @default.
• W3023843563 creator A5086061723 @default.
• W3023843563 date "2021-11-01" @default.
• W3023843563 modified "2023-10-14" @default.
• W3023843563 title "Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions" @default.
• W3023843563 cites W1507901999 @default.
• W3023843563 cites W1970950689 @default.
• W3023843563 cites W1977496278 @default.
• W3023843563 cites W2001141328 @default.
• W3023843563 cites W2019711853 @default.
• W3023843563 cites W2021173510 @default.
• W3023843563 cites W2021880490 @default.
• W3023843563 cites W2036464173 @default.
• W3023843563 cites W2037915750 @default.
• W3023843563 cites W2053186076 @default.
• W3023843563 cites W2057151160 @default.
• W3023843563 cites W2062112832 @default.
• W3023843563 cites W2063011414 @default.
• W3023843563 cites W2076356585 @default.
• W3023843563 cites W2079746361 @default.
• W3023843563 cites W2098357151 @default.
• W3023843563 cites W2102442664 @default.
• W3023843563 cites W2102831150 @default.
• W3023843563 cites W2111501574 @default.
• W3023843563 cites W2115157373 @default.
• W3023843563 cites W2128985829 @default.
• W3023843563 cites W2129066856 @default.
• W3023843563 cites W2132914434 @default.
• W3023843563 cites W2133585870 @default.
• W3023843563 cites W2134785517 @default.
• W3023843563 cites W2140551998 @default.
• W3023843563 cites W2149723023 @default.
• W3023843563 cites W2154941270 @default.
• W3023843563 cites W2158402998 @default.
• W3023843563 cites W2165319922 @default.
• W3023843563 cites W2339449984 @default.
• W3023843563 cites W2552354243 @default.
• W3023843563 cites W2794520086 @default.
• W3023843563 cites W2807407269 @default.
• W3023843563 cites W2897433657 @default.
• W3023843563 cites W2945218850 @default.
• W3023843563 cites W4237171445 @default.
• W3023843563 cites W4245510718 @default.
• W3023843563 doi "https://doi.org/10.1109/tcbb.2020.2992605" @default.
• W3023843563 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32386162" @default.
• W3023843563 hasPublicationYear "2021" @default.
• W3023843563 type Work @default.
• W3023843563 sameAs 3023843563 @default.
• W3023843563 citedByCount "6" @default.
• W3023843563 countsByYear W30238435632020 @default.
• W3023843563 countsByYear W30238435632021 @default.
• W3023843563 countsByYear W30238435632022 @default.
• W3023843563 countsByYear W30238435632023 @default.
• W3023843563 crossrefType "journal-article" @default.
• W3023843563 hasAuthorship W3023843563A5055139178 @default.
• W3023843563 hasAuthorship W3023843563A5079234443 @default.
• W3023843563 hasAuthorship W3023843563A5086061723 @default.
• W3023843563 hasConcept C119857082 @default.
• W3023843563 hasConcept C121608353 @default.
• W3023843563 hasConcept C124101348 @default.
• W3023843563 hasConcept C154945302 @default.
• W3023843563 hasConcept C2779013556 @default.
• W3023843563 hasConcept C41008148 @default.
• W3023843563 hasConcept C54355233 @default.
• W3023843563 hasConcept C70721500 @default.
• W3023843563 hasConcept C73555534 @default.
• W3023843563 hasConcept C86803240 @default.
• W3023843563 hasConcept C91682802 @default.
• W3023843563 hasConceptScore W3023843563C119857082 @default.
• W3023843563 hasConceptScore W3023843563C121608353 @default.
• W3023843563 hasConceptScore W3023843563C124101348 @default.
• W3023843563 hasConceptScore W3023843563C154945302 @default.
• W3023843563 hasConceptScore W3023843563C2779013556 @default.
• W3023843563 hasConceptScore W3023843563C41008148 @default.
• W3023843563 hasConceptScore W3023843563C54355233 @default.
• W3023843563 hasConceptScore W3023843563C70721500 @default.
• W3023843563 hasConceptScore W3023843563C73555534 @default.
• W3023843563 hasConceptScore W3023843563C86803240 @default.
• W3023843563 hasConceptScore W3023843563C91682802 @default.
• W3023843563 hasFunder F4320316880 @default.
• W3023843563 hasFunder F4320321001 @default.
• W3023843563 hasFunder F4320337111 @default.
• W3023843563 hasIssue "6" @default.
• W3023843563 hasLocation W30238435631 @default.
• W3023843563 hasLocation W30238435632 @default.
• W3023843563 hasOpenAccess W3023843563 @default.
• W3023843563 hasPrimaryLocation W30238435631 @default.
• W3023843563 hasRelatedWork W1999627569 @default.
• W3023843563 hasRelatedWork W2001281435 @default.
• W3023843563 hasRelatedWork W2009478079 @default.
• W3023843563 hasRelatedWork W2380998760 @default.
• W3023843563 hasRelatedWork W2961085424 @default.
• W3023843563 hasRelatedWork W4286629047 @default.
• W3023843563 hasRelatedWork W4306321456 @default.
• W3023843563 hasRelatedWork W4306674287 @default.

• next