Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloLogic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables
Anno di pubblicazione2016
FormatoElettronico
Autore/iStefano Parodi, Chiara Manneschi, Damiano Verda, Enrico Ferrari, Marco Muselli
Affiliazioni autoriNational Research Council of Italy (Italy), Italian Institute of Technology (Italy), Rulex Inc (USA), Rulex Inc (USA), National Research Council of Italy (Italy)
Autori CNR e affiliazioni
  • STEFANO PARODI
  • MARCO MUSELLI
Lingua/e
  • inglese
AbstractThis study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms (k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene (XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da1
Pagine a12
Pagine totali12
RivistaHealth informatics journal (Online)
Attiva dal 1998
Editore: Sage Publications. - London
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1741-2811
Titolo chiave: Health informatics journal (Online)
Titolo proprio: Health informatics journal. (Online)
Numero volume della rivista-
Fascicolo della rivista-
DOI10.1177/1460458216655188
Verificato da refereeSì: Internazionale
Stato della pubblicazionePreprint
Indicizzazione (in banche dati controllate)-
Parole chiaveartificial neural network, cancer prognosis, Decision Tree, Hodgkin's lymphoma, Logic Learning Machine, Support Vector Machine
Link (URL, URI)http://jhi.sagepub.com
Titolo parallelo-
Data di accettazione24/05/2016
Note/Altre informazioni-
Strutture CNR
  • IEIIT — IEIIT - Sede secondaria di Genova
Moduli CNR-
Progetti Europei-
Allegati
Logic Learning Machine and Hodgkin's lymphoma (documento privato )
Descrizione: Articolo in epub
Tipo documento: application/pdf