Machine learning-based analysis of glioma grades reveals co-enrichment

Basit Öğe Kaydı
dc.contributor.authorGarbulowski, Mateusz
dc.contributor.authorSmolinska, Karolina
dc.contributor.authorÇabuk, Uğur
dc.contributor.authorYones, Sara A.
dc.contributor.authorCelli, Ludovica
dc.contributor.authorYaz, Esma Nur
dc.contributor.authorBarrenäs, Fredrik
dc.contributor.authorDiamanti, Klev
dc.contributor.authorWadelius, Claes
dc.contributor.authorKomorowski, Jan
dc.date.accessioned2022-03-04T08:28:28Z
dc.date.available2022-03-04T08:28:28Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Fen Bilimleri Enstitüsü, Biyomedikal Mühendisliği ve Biyoenformatik Ana Bilim Dalı
dc.description.abstractGliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.
dc.identifier.citationGarbulowski, M., Smolinska, K., Çabuk, U., Yones, S. A., Celli, L., Yaz, E. N. ... Komorowski, J. (2022). Machine learning-based analysis of glioma grades reveals co-enrichment. Cancers, 14(4). https://doi.org/10.3390/cancers14041014
dc.identifier.doi10.3390/cancers14041014
dc.identifier.issn2072-6694
dc.identifier.issue4
dc.identifier.pmid35205761
dc.identifier.scopus2-s2.0-85124972494
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/cancers14041014
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9099
dc.identifier.volume14
dc.identifier.wos000767610500001en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorYaz, Esma Nur
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofCancersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectGlioma
dc.subjectMachine Learning
dc.subjectBatch Effect
dc.subjectTCGA
dc.subjectCo-Enrichment
dc.subjectRough Sets
dc.titleMachine learning-based analysis of glioma grades reveals co-enrichment
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Yaz-Esma-2022.pdf
Boyut:
3.35 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Fen Bilimleri Enstitüsü Dergi Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu