Matches in SemOpenAlex for { <https://semopenalex.org/work/W2917208497> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W2917208497 abstract "DNA-Microarray technology simultaneously monitors the expression profiles of thousands of genes over various experimental conditions. Identifying co-expressed genes and coherent patterns is the central goal of clustering process, and it is an important task in bioinformatics as it helps biologists to gain insights on gene functions, because genes with similar functions exhibit similar expression patterns. Clustering process is used to identify cancer subtypes based on gene expression and DNA methylation datasets, since cancer subtype information is critically important for understanding tumor heterogeneity, detecting previously unknown clusters of biological samples, which are usually associated with unknown types of cancer will, in turn, gives way to prescribe more effective treatments for patients, as cancer varying subtypes often respond disparately to the same treatment. While DNA methylation database is kind of extremely large-scale datasets, running time still remains a major challenge. Actually, almost all the proposed algorithms are stochastic, this characteristic turns out a great issue when it comes to deal with high-dimensional biological datasets, since the biologist needs to run the algorithm several times, before taking out the mean of the results obtained in each time, hence they usually require large amounts of computational time. The proposed clustering algorithm is purely non-stochastic, it is able to accurately identify a set of biologically relevant clusters in large-scale DNA datasets, and therefore the biologist needs to run the algorithm just once." @default.
- W2917208497 created "2019-03-02" @default.
- W2917208497 creator A5030944764 @default.
- W2917208497 creator A5042785802 @default.
- W2917208497 date "2019-01-01" @default.
- W2917208497 modified "2023-09-23" @default.
- W2917208497 title "A Non-stochastic Method for Clustering of Big Genomic Data" @default.
- W2917208497 cites W1831033366 @default.
- W2917208497 cites W2141012957 @default.
- W2917208497 cites W2169741256 @default.
- W2917208497 cites W2970838456 @default.
- W2917208497 doi "https://doi.org/10.1007/978-3-030-12048-1_10" @default.
- W2917208497 hasPublicationYear "2019" @default.
- W2917208497 type Work @default.
- W2917208497 sameAs 2917208497 @default.
- W2917208497 citedByCount "0" @default.
- W2917208497 crossrefType "book-chapter" @default.
- W2917208497 hasAuthorship W2917208497A5030944764 @default.
- W2917208497 hasAuthorship W2917208497A5042785802 @default.
- W2917208497 hasConcept C104317684 @default.
- W2917208497 hasConcept C111919701 @default.
- W2917208497 hasConcept C121332964 @default.
- W2917208497 hasConcept C124101348 @default.
- W2917208497 hasConcept C150194340 @default.
- W2917208497 hasConcept C154945302 @default.
- W2917208497 hasConcept C177264268 @default.
- W2917208497 hasConcept C190727270 @default.
- W2917208497 hasConcept C199360897 @default.
- W2917208497 hasConcept C201797286 @default.
- W2917208497 hasConcept C2778755073 @default.
- W2917208497 hasConcept C41008148 @default.
- W2917208497 hasConcept C54355233 @default.
- W2917208497 hasConcept C60644358 @default.
- W2917208497 hasConcept C62520636 @default.
- W2917208497 hasConcept C70721500 @default.
- W2917208497 hasConcept C73555534 @default.
- W2917208497 hasConcept C86803240 @default.
- W2917208497 hasConcept C98045186 @default.
- W2917208497 hasConceptScore W2917208497C104317684 @default.
- W2917208497 hasConceptScore W2917208497C111919701 @default.
- W2917208497 hasConceptScore W2917208497C121332964 @default.
- W2917208497 hasConceptScore W2917208497C124101348 @default.
- W2917208497 hasConceptScore W2917208497C150194340 @default.
- W2917208497 hasConceptScore W2917208497C154945302 @default.
- W2917208497 hasConceptScore W2917208497C177264268 @default.
- W2917208497 hasConceptScore W2917208497C190727270 @default.
- W2917208497 hasConceptScore W2917208497C199360897 @default.
- W2917208497 hasConceptScore W2917208497C201797286 @default.
- W2917208497 hasConceptScore W2917208497C2778755073 @default.
- W2917208497 hasConceptScore W2917208497C41008148 @default.
- W2917208497 hasConceptScore W2917208497C54355233 @default.
- W2917208497 hasConceptScore W2917208497C60644358 @default.
- W2917208497 hasConceptScore W2917208497C62520636 @default.
- W2917208497 hasConceptScore W2917208497C70721500 @default.
- W2917208497 hasConceptScore W2917208497C73555534 @default.
- W2917208497 hasConceptScore W2917208497C86803240 @default.
- W2917208497 hasConceptScore W2917208497C98045186 @default.
- W2917208497 hasLocation W29172084971 @default.
- W2917208497 hasOpenAccess W2917208497 @default.
- W2917208497 hasPrimaryLocation W29172084971 @default.
- W2917208497 hasRelatedWork W1488479429 @default.
- W2917208497 hasRelatedWork W1541132003 @default.
- W2917208497 hasRelatedWork W1589891601 @default.
- W2917208497 hasRelatedWork W1698298084 @default.
- W2917208497 hasRelatedWork W1831210132 @default.
- W2917208497 hasRelatedWork W2043327336 @default.
- W2917208497 hasRelatedWork W2124052810 @default.
- W2917208497 hasRelatedWork W2131860595 @default.
- W2917208497 hasRelatedWork W2134179844 @default.
- W2917208497 hasRelatedWork W2134809324 @default.
- W2917208497 hasRelatedWork W2163217357 @default.
- W2917208497 hasRelatedWork W2176182147 @default.
- W2917208497 hasRelatedWork W2317695591 @default.
- W2917208497 hasRelatedWork W2349998653 @default.
- W2917208497 hasRelatedWork W2735812438 @default.
- W2917208497 hasRelatedWork W2738748219 @default.
- W2917208497 hasRelatedWork W300185981 @default.
- W2917208497 hasRelatedWork W3084559954 @default.
- W2917208497 hasRelatedWork W3152847616 @default.
- W2917208497 hasRelatedWork W2111185752 @default.
- W2917208497 isParatext "false" @default.
- W2917208497 isRetracted "false" @default.
- W2917208497 magId "2917208497" @default.
- W2917208497 workType "book-chapter" @default.