This HTML5 document contains 36 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n2https://kar.kent.ac.uk/id/eprint/
wdrshttp://www.w3.org/2007/05/powder-s#
n21http://purl.org/ontology/bibo/status/
dchttp://purl.org/dc/elements/1.1/
rdfshttp://www.w3.org/2000/01/rdf-schema#
n9https://kar.kent.ac.uk/id/subject/
n13https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/
n3http://eprints.org/ontology/
bibohttp://purl.org/ontology/bibo/
n17https://kar.kent.ac.uk/76421/
n6https://kar.kent.ac.uk/id/publication/
n20https://kar.kent.ac.uk/id/org/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n19doi:10.1016/
n11https://kar.kent.ac.uk/id/eprint/76421#
owlhttp://www.w3.org/2002/07/owl#
n4https://kar.kent.ac.uk/id/document/
n8https://kar.kent.ac.uk/id/
xsdhhttp://www.w3.org/2001/XMLSchema#
n14https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/
n10https://kar.kent.ac.uk/id/person/

Statements

Subject Item
n2:76421
rdf:type
n3:ArticleEPrint bibo:AcademicArticle bibo:Article n3:EPrint
rdfs:seeAlso
n17:
owl:sameAs
n19:j.artmed.2019.05.002
n3:hasAccepted
n4:3187426
n3:hasDocument
n4:3187527 n4:3187659 n4:3187660 n4:3187661 n4:3187426 n4:3187662
dc:hasVersion
n4:3187426
dcterms:title
Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles
wdrs:describedby
n13:export_kar_RDFN3.n3 n14:export_kar_RDFN3.n3
dcterms:date
2019-05-15
dcterms:creator
n10:ext-4e7d79bd0cff57c78dba99062a623345 n10:ext-c347d2a78866610508848bf80fcf5dcf n10:ext-8abf44056bf6c8add34822522d228409 n10:ext-2c26eb94c84d5a544acc223ac43a6303 n10:ext-df28b453cfa2d69a50186671c691446d n10:ext-45549115a972a57cd8fc7cc7fb6212f5 n10:ext-4b9e6494bf49a9a88aee0280e9daebf0 n10:ext-14eb4fc99d1f0f7a5d6b91c3da94b12d n10:ext-d.soria@kent.ac.uk
bibo:status
n21:peerReviewed n21:published
dcterms:publisher
n20:ext-f308aad1ef8f70546c3a197f104f2ad5
bibo:abstract
Breast Cancer is one of the most common causes of cancer death in women, representing a very complex disease with varied molecular alterations. To assist breast cancer prognosis, the classification of patients into biological groups is of great significance for treatment strategies. Recent studies have used an ensemble of multiple clustering algorithms to elucidate the most characteristic biological groups of breast cancer. However, the combination of various clustering methods resulted in a number of patients remaining unclustered. Therefore, a framework still needs to be developed which can assign as many unclustered (i.e. biologically diverse) patients to one of the identified groups in order to improve classification. Therefore, in this paper we develop a novel classification framework which introduces a new ensemble classification stage after the ensemble clustering stage to target the unclustered patients. Thus, a step-by-step pipeline is introduced which couples ensemble clustering with ensemble classification for the identification of core groups, data distribution in them and improvement in final classification results by targeting the unclustered data. The proposed pipeline is employed on a novel real world breast cancer dataset and subsequently its robustness and stability are examined by testing it on standard datasets. The results show that by using the presented framework, an improved classification is obtained. Finally, the results have been verified using statistical tests, visualisation techniques, cluster quality assessment and interpretation from clinical experts.
dcterms:isPartOf
n6:ext-09333657 n8:repository
dcterms:subject
n9:R858
bibo:authorList
n11:authors
bibo:volume
97