This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2013: Bioinformatics

Open Access Research

Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients

Davide Cangelosi1, Marco Muselli2, Stefano Parodi2, Fabiola Blengio1, Pamela Becherini1, Rogier Versteeg3, Massimo Conte4 and Luigi Varesio1*

Author Affiliations

1 Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy

2 Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa 16149, Italy

3 Department of Human Genetics, Academic Medical Center, University of Amsterdam, Meibergdreef 15, Amsterdam 1100, The Netherlands

4 Department of Hematology-Oncology, Gaslini Institute, Largo Gaslini 5, Genoa 16147, Italy

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BMC Bioinformatics 2014, 15(Suppl 5):S4  doi:10.1186/1471-2105-15-S5-S4

Published: 6 May 2014



Cancer patient's outcome is written, in part, in the gene expression profile of the tumor. We previously identified a 62-probe sets signature (NB-hypo) to identify tissue hypoxia in neuroblastoma tumors and showed that NB-hypo stratified neuroblastoma patients in good and poor outcome [1]. It was important to develop a prognostic classifier to cluster patients into risk groups benefiting of defined therapeutic approaches. Novel classification and data discretization approaches can be instrumental for the generation of accurate predictors and robust tools for clinical decision support. We explored the application to gene expression data of Rulex, a novel software suite including the Attribute Driven Incremental Discretization technique for transforming continuous variables into simplified discrete ones and the Logic Learning Machine model for intelligible rule generation.


We applied Rulex components to the problem of predicting the outcome of neuroblastoma patients on the bases of 62 probe sets NB-hypo gene expression signature. The resulting classifier consisted in 9 rules utilizing mainly two conditions of the relative expression of 11 probe sets. These rules were very effective predictors, as shown in an independent validation set, demonstrating the validity of the LLM algorithm applied to microarray data and patients' classification. The LLM performed as efficiently as Prediction Analysis of Microarray and Support Vector Machine, and outperformed other learning algorithms such as C4.5. Rulex carried out a feature selection by selecting a new signature (NB-hypo-II) of 11 probe sets that turned out to be the most relevant in predicting outcome among the 62 of the NB-hypo signature. Rules are easily interpretable as they involve only few conditions.

Furthermore, we demonstrate that the application of a weighted classification associated with the rules improves the classification of poorly represented classes.


Our findings provided evidence that the application of Rulex to the expression values of NB-hypo signature created a set of accurate, high quality, consistent and interpretable rules for the prediction of neuroblastoma patients' outcome. We identified the Rulex weighted classification as a flexible tool that can support clinical decisions. For these reasons, we consider Rulex to be a useful tool for cancer classification from microarray gene expression data.

Logic Learning Machine; Attribute Driven Incremental Discretization; Explicit rules; NB-hypo-II signature; Neuroblastoma; Hypoxia; Classifier; Weighted Classification