Multiomics Topic Modeling for Breast Cancer Classification.
chr14q32
miRNA expression regulation
miRNAs
multiomics
stochastic block modeling
topic modeling
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
23 Feb 2022
23 Feb 2022
Historique:
received:
11
02
2022
accepted:
18
02
2022
entrez:
10
3
2022
pubmed:
11
3
2022
medline:
11
3
2022
Statut:
epublish
Résumé
The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of 'omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs, and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or "topics" that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development and are associated to the survival probability.
Identifiants
pubmed: 35267458
pii: cancers14051150
doi: 10.3390/cancers14051150
pmc: PMC8909787
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Italian Ministry of Education, University and Research (MIUR) (L.232/2016)
ID : Departments of Excellence 2018--2022
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