Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloLogic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients
Anno di pubblicazione2013
FormatoElettronico
Autore/iD. Cangelosi, F. Blengio, R. Versteeg, A. Eggert, A. Garaventa, C. Gambini, M. Conte, A. Eva, M. Muselli, L. Varesio
Affiliazioni autoriD. Cangelosi, F. Blengio, R. Versteeg, A. Eggert, A. Garaventa, C. Gambini, M. Conte, A. Eva, L. Varesio: Istituto Giannina Gaslini, Genova; M. Muselli: CNR-IEIIT, via De Marini, 6, 16149, Genoa, Italy.
Autori CNR e affiliazioni
  • MARCO MUSELLI
Lingua/e
  • inglese
AbstractBACKGROUND: Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. RESULTS: Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. CONCLUSIONS: The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine daS12
Pagine a-
Pagine totali-
RivistaBMC bioinformatics
Attiva dal 2000
Editore: BioMed Central, - [London]
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1471-2105
Titolo chiave: BMC bioinformatics
Titolo proprio: BMC bioinformatics
Titolo abbreviato: BMC bioinformatics
Titoli alternativi:
  • BioMed Central bioinformatics
  • Bioinformatics
Numero volume della rivista14
Fascicolo della rivista7
DOI10.1186/1471-2105-14-S7-S12
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • PubMed (Codice:23815266)
  • ISI Web of Science (WOS) (Codice:000318869400012)
Parole chiaveLogic Learning Machine, Explicit rules, NB-hypo, Stability, Neuroblastoma, Hypoxia, Classifier
Link (URL, URI)http://www.biomedcentral.com/1471-2105/14/S7/S12
Titolo parallelo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — IEIIT - Sede secondaria di Genova
Moduli CNR
    Progetti Europei-
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