http://www.cnr.it/ontology/cnr/individuo/prodotto/ID280244
Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients (Articolo in rivista)
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- Label
- Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients (Articolo in rivista) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Alternative label
D. CANGELOSI, M. MUSELLI, S. PARODI, F. BLENGIO, P. BECHERINI, R. VERSTEEG, M. CONTE, L. VARESIO (2014)
Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients
in BMC bioinformatics
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- D. CANGELOSI, M. MUSELLI, S. PARODI, F. BLENGIO, P. BECHERINI, R. VERSTEEG, M. CONTE, L. VARESIO (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.biomedcentral.com/1471-2105/15/S5/S4 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- D. CANGELOSI, F. BLENGIO, P. BECHERINI, L. VARESIO: Laboratory of Molecular Biology, Gaslini Institute, Largo Gaslini 5, 16147 Genoa, Italy.
M. MUSELLI, S. PARODI: Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa 16149, Italy.
R. VERSTEEG: Department of Human Genetics, Academic Medical Center, University of Amsterdam, Meibergdreef 15, Amsterdam 1100, The Netherlands.
M. CONTE: Department of Hematology-Oncology, Gaslini Institute, Largo Gaslini 5, Genoa 16147, Italy (literal)
- Titolo
- Use of Attribute Driven Incremental Discretization and Logic Learning Machine to build a prognostic classifier for neuroblastoma patients (literal)
- Abstract
- Background: 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.
Results: 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.
Conclusions: 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. (literal)
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