Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables

Author:

Parodi Stefano12,Manneschi Chiara32,Verda Damiano2,Ferrari Enrico2,Muselli Marco1

Affiliation:

1. National Research Council of Italy, Italy

2. Rulex Inc, USA

3. Italian Institute of Technology, Italy

Abstract

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin’s lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin’s lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin’s lymphoma patients.

Publisher

SAGE Publications

Subject

Health Informatics

Reference43 articles.

1. Parodi S, Stagnaro E. Hodgkin’s Disease Worldwide – Incidence, Mortality, Survival, Prevalence and Time Trend. New York: Nova Science Publisher, 2009, pp. 1–8.

2. Recent Advances in the Pathobiology of Hodgkin's Lymphoma: Potential Impact on Diagnostic, Predictive, and Therapeutic Strategies

3. Hodgkin lymphoma: 2014 update on diagnosis, risk-stratification, and management

4. A Prognostic Score for Advanced Hodgkin's Disease

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