Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods

Damiano Verda, Stefano Parodi, Enrico Ferrari, Marco Muselli

Research output: Contribution to journalArticle


BACKGROUND: 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.

Original languageEnglish
Pages (from-to)390
JournalBMC Bioinformatics
Issue numberSuppl 9
Publication statusPublished - Nov 22 2019



  • Adult
  • Child
  • Databases, Genetic
  • Female
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Logic
  • Machine Learning
  • Male
  • Neoplasms/diagnosis
  • Neural Networks, Computer
  • ROC Curve

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