Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloAnalyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods
Anno di pubblicazione2019
Formato-
Autore/iVerda D.; Parodi S.; Ferrari, E.; Muselli M.
Affiliazioni autoriRulex Inc., Newton, MA, USA; Epidemiology and Biostatistics Unit, IRCCS, Istituto Giannina Gaslini, Genoa, Italy; Institute of Electronics, Computer and Telecommunication Engineering National Research Council of Italy, Genoa, Italy
Autori CNR e affiliazioni
  • MARCO MUSELLI
Lingua/e
  • inglese
AbstractBackground: Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area under an sROC curve (sAUC). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k-nearest neighbor classifier. Results: LLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98-1.0) and outperformed any other method except SVM. Conclusions: LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da390
Pagine a-
Pagine totali13
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 rivista20
Fascicolo della rivista-
DOI10.1186/s12859-019-2953-8
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000503868200007)
  • Scopus (Codice:2-s2.0-85075473315)
Parole chiaveLogic learning machine, Neural network, Support vector machine, Decision tree, K-nearest neighbor classifier, Gene expression, Microarrays, Cancer, Diagnosis, Prognosis
Link (URL, URI)https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2953-8
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati