The multilayer community structure of medulloblastoma

Document typeConference report
Defense date2021-05
PublisherBarcelona Supercomputing Center
Rights accessOpen Access
Abstract
Biomedical multilayer networks offer a wide range of
possibilities for the interpretation of the molecular basis of
diseases; a particularly challenging task in the case of rare
diseases, where the number of cases is small in comparison
with the size of the associated multi-omics datasets. In this
work, we develop a dimensionality reduction methodology to
identify the minimal set of genes that characterize disease
subgroups based on their persistent association in the
multilayer network at different levels of resolution.
We apply this approach to the study of a cohort of patients
affected by medulloblastoma, a childhood brain tumor, using
proteogenomic data. Our approach is able to recapitulate
known medulloblastoma subtypes (accuracy > 94%) and
offers a clear characterization of the associated gene
functions, with the downstream implications for diagnosis
and therapeutic interventions.
We verified the general applicability of our method by
applying it to an independent dataset, achieving very high
performances (accuracy > 98%). Overall, this approach opens
the door to a new generation of multilayer-based methods
able to overcome the specific dimensionality limitations of
the rare disease datasets.
CitationNuñez Carpintero, I.; Cirillo, D.; Valencia, A. The multilayer community structure of medulloblastoma. A: . Barcelona Supercomputing Center, 2021, p. 52-53.
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