The multilayer community structure of medulloblastoma
| Autor: | Iker Núñez-Carpintero, Marianyela Petrizzelli, Davide Cirillo, Alfonso Valencia, Andrei Zinovyev |
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| Přispěvatelé: | Barcelona Supercomputing Center, Institut Curie [Paris], ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 826121,iPaediatricCurie |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Proteomics Informàtica::Aplicacions de la informàtica::Bioinformàtica [Àrees temàtiques de la UPC] Computer science Science 02 engineering and technology Disease Computational biology medulloblastoma Article 03 medical and health sciences medicine cancer Medulloblastoma Multidisciplinary business.industry Multilayer networks Dimensionality reduction 021001 nanoscience & nanotechnology medicine.disease [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] Multi-omics datasets 3. Good health Rare diseases 030104 developmental biology Cancer systems biology network Personalized medicine Malalties rares 0210 nano-technology business Cancer Systems Biology Molecular and cell biochemistry Proteogenomic data Curse of dimensionality Childhood brain tumor |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) iScience iScience, Elsevier, 2021, 24 (4), pp.102365. ⟨10.1016/j.isci⟩ iScience, Vol 24, Iss 4, Pp 102365-(2021) |
| ISSN: | 2589-0042 |
| DOI: | 10.1016/j.isci⟩ |
| Popis: | Summary Multilayer networks allow interpreting the molecular basis of diseases, which is particularly challenging in rare diseases where the number of cases is small compared 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 multilayer network communities. We use this approach to the study of medulloblastoma, a childhood brain tumor, using proteogenomic data. Our approach is able to recapitulate known medulloblastoma subgroups (accuracy >94%) and provide a clear characterization of gene associations, with the downstream implications for diagnosis and therapeutic interventions. We verified the general applicability of our method on an independent medulloblastoma dataset (accuracy >98%). This approach opens the door to a new generation of multilayer network-based methods able to overcome the specific dimensionality limitations of rare disease datasets. Graphical abstract Highlights • The molecular interpretation of rare diseases is a challenging task • Multilayer networks allow patient stratification and explainability • We identify subgroup-specific genes and multilayer associations in medulloblastoma • Multilayer community analysis enables the molecular interpretation of rare diseases Proteomics; Cancer Systems Biology; Cancer |
| Databáze: | OpenAIRE |
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