Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloOn the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples
Anno di pubblicazione2021
Formato
  • Elettronico
  • Cartaceo
Autore/iMachicao J.; Craighero F.; Maspero D.; Angaroni F.; Damiani C.; Graudenzi A.; Antoniotti M.; Bruno O.M.
Affiliazioni autoriUniv Sao Paulo, Sao Carlos Inst Phys, Sao Carlos, Brazil Univ Sao Paulo, Sch Engn, Sao Carlos, Brazil Univ Milano Bicocca, Dept Informat Syst & Commun, Milan, Italy Consiglio Nazl Ric IBFM CNR, Inst Mol Bioimaging & Physiol, Milan, Italy Univ Milano Bicocca, Dept Biotechnol & Biosci, Milan, Italy Sysbio Ctr Syst Biol, Milan, Italy Univ Milano Bicocca, Bicocca Bioinformat Biostat & Bioimaging Ctr B4, Milan, Italy
Autori CNR e affiliazioni
  • DAVIDE MASPERO
  • ALEX GRAUDENZI
Lingua/e
  • inglese
AbstractBackground: The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis. Introduction: The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective di-agnostic and prognostic strategies. Methods: We explore the possibility of exploiting the topological properties of sample-specific met-abolic networks as features in a supervised classification task. Such networks are obtained by pro-jecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample. Results: We show the classification results on a labeled breast cancer dataset from the TCGA data-base, including 210 samples (cancer vs. normal). In particular, we investigate how the performance is affected by a threshold-based pruning of the networks by comparing Artificial Neural Networks, Support Vector Machines and Random Forests. Interestingly, the best classification performance is achieved within a small threshold range for all methods, suggesting that it might represent an effec-tive choice to recover useful information while filtering out noise from data. Overall, the best accu-racy is achieved with SVMs, which exhibit performances similar to those obtained when gene ex-pression profiles are used as features. Conclusion: These findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da88
Pagine a97
Pagine totali9
RivistaCurrent genomics (Online)
Attiva dal 2000
Editore: Bentham Science Publishers - Hilversum
Paese di pubblicazione: Paesi Bassi
Lingua: inglese
ISSN: 1875-5488
Titolo chiave: Current genomics (Online)
Titolo proprio: Current genomics. (Online)
Numero volume della rivista22
Fascicolo della rivista2
DOI10.2174/1389202922666210301084151
Verificato da referee-
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000641581900003)
Parole chiaveMetabolic networks, cancer sample classification, machine learning, RNA-seq data, topological properties, network pruning.
Link (URL, URI)https://www.eurekaselect.com/191876/article
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IBFM — Istituto di bioimmagini e fisiologia molecolare
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati