Estimating Female Labor Force Participation through Statistical and Machine Learning Methods: A Comparison

Zambrano, Omar; S, Claudio M. Rocco; Muselli, Marco

doi:10.1007/978-3-540-72821-4_5

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…LLM, SNN and SC have been successfully used in different applications: from reliability evaluation of complex systems [38] to prediction of social phenomena [39], form bulk electric assessment [40] to analysis of biomedical data [35,36,41,42]. In particular, in this last field the ability of generating models described by intelligible rules have carried out many advantages, allowing to extract important knowledge from available data.…”

Section: Discussionmentioning

confidence: 99%

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

Cangelosi¹,

Blengio²,

Versteeg

et al. 2013

BMC Bioinformatics

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BackgroundNeuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting.ResultsShadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging.ConclusionsThe novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new class of poor outcome patients that could benefit from new therapies potentially targeting tumor hypoxia or its consequences.

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Section: Discussionmentioning

confidence: 99%

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

Cangelosi¹,

Blengio²,

Versteeg

et al. 2013

BMC Bioinformatics

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“…LLM is a novel and efficient implementation of the Switching Neural Network (SNN) model [ 33 ] trained through an optimized version of the SC algorithm. LLM, SNN and SC have been successfully used in different applications: from reliability evaluation of complex systems [ 34 ] to prediction of social phenomena [ 35 ], form bulk electric assessment [ 36 ] to analysis of biomedical data [ 15 , 31 , 32 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

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

Cangelosi¹,

Muselli

Parodi

et al. 2014

BMC Bioinformatics

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BackgroundCancer 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.ResultsWe 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.ConclusionsOur 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.

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Estimating Female Labor Force Participation through Statistical and Machine Learning Methods: A Comparison

Cited by 2 publications

References 9 publications

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

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

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