Paper Info
Title
Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.
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.
Year
DOI
Venue
2018
10.1177/1460458216655188
HEALTH INFORMATICS JOURNAL
Keywords
Field
DocType
artificial neural network,cancer prognosis,Decision Tree,Hodgkin's lymphoma,Logic Learning Machine,Support Vector Machine
Decision tree,Text mining,Hodgkin's lymphoma,Expression (mathematics),Computer science,Support vector machine,XIST,Logic learning machine,Artificial intelligence,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
24.0
1.0
1460-4582
Citations 
PageRank 
References 
0
0.34
10
Authors
5
Name
Order
Citations
PageRank
Stefano Parodi1422.68
Chiara Manneschi221.04
Damiano Verda3172.64
Enrico Ferrari4163.55
Marco Muselli522024.97