Abstract
BackgroundLogic 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.ResultsLLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98–1.0) and outperformed any other method except SVM.ConclusionsLLM 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.
Highlights
Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules
These results indicate that LLM could be a new powerful and flexible tool for the analysis of gene expression data in Oncology setting
LLM is an innovative method of supervised analysis that can identify simple and intelligible rules for classification tasks
Summary
Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. LLM was applied to extract few highly discriminant rules from a signature of 62 genes related to hypoxic condition for the prognosis of neuroblastoma, a highly fatal childhood cancer [2] In such analysis LLM outperformed standard methods of machine learning, including: Decision Tree (DT), Artificial Neural Network (ANN) and k-Nearest Neighbor classifier (kNN). The capability of LLM to exploit the complex correlation structure of highly dimensional gene expression data for feature selection tasks and to combine information from clinical features and gene expression for classification purposes was reported in the analysis of both simulated and real data sets [1, 6]. These results indicate that LLM could be a new powerful and flexible tool for the analysis of gene expression data in Oncology setting
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