Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods.

Abstract:

BACKGROUND:Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area under an sROC curve (sAUC). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k-nearest neighbor classifier. RESULTS:LLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98-1.0) and outperformed any other method except SVM. CONCLUSIONS:LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Verda D,Parodi S,Ferrari E,Muselli M

doi

10.1186/s12859-019-2953-8

subject

Has Abstract

pub_date

2019-11-22 00:00:00

pages

390

issue

Suppl 9

issn

1471-2105

pii

10.1186/s12859-019-2953-8

journal_volume

20

pub_type

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