Using a Fuzzy Decision Tree Ensemble for Tumor Classification from Gene Expression Data

Machine learning techniques are useful tools that can help us in the knowledge extraction from gene expression data in biological systems. In this paper two machine learning techniques are applied to tumor datasets based on gene expression data. Both techniques are based on a fuzzy decision tree ensemble and are used to carry out the classification and selection of features on datasets. The classification accuracies obtained both when we use all genes to classify and when we only use the selected genes are high. However, in this second case the result also increases the interpretability of the solution provided by the technique. Additionally, the feature selection technique provides a ranking of importance of genes and a partitioning of the domains of the genes. 1 TUMOR CLASSIFICATION FROM GENE EXPRESSION DATA The challenge of cancer treatment has been to target specific therapies to pathogenetically distinct tumor types, to maximize efficacy and minimize toxicity. Improvements in cancer classification have thus been central to advances in cancer treatment. Cancer classification is divided into two challenges: class discovery and class prediction. Class discovery refers to defining previously unrecognized tumor subtypes. Class prediction refers to the assignment of particular tumor examples to already-defined classes. In the early days, cancer classification has been relying on subjective judgment from experienced pathologists. When microarray technology was discovered began to be applied to cancer diagnosis. The most important application of the microarray technique is to discriminate the normal and cancerous tissue samples according to their expression levels, identify a small subset of genes that are responsible for the disease and to discover potential drugs, (Ghoraia et al., 2012). Experimental techniques based on oligonucleotide or cDNA arrays now allow the expression level of thousands of genes to be monitored in parallel (Alon et al., 1999). To use the full potential of such experiments, it is important to develop the ability to process and extract useful information from large gene expression datasets. Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Gene expression data can help in better understanding of cancer. Normal cells can evolve into malignant cancer cells through a series of mutations in genes that control the cell cycle, apoptosis, and genome integrity, to name only a few. As determination of cancer type and stage is often crucial to the assignment of appropriate treatment (Golub et al., 1999), a central goal of the analysis of gene expression data is the identification of sets of genes that can serve, via expression profiling assays, as classification or diagnosis platforms. Another important purpose of gene expression studies is to improve understanding of cellular responses to drug treatment. Expression profiling assays performed before, during and after treatment, are aimed at identifying drug responsive genes, indications of treatment outcomes, and at identifying potential drug targets (Clarke et al., 1999). More generally, complete profiles can be considered as a potential basis for classification of treatment progression or other trends in the evolution of the treated cells. 320 Cadenas J., Garrido M. , Martínez R. , A. Pelta D. and P. Bonissone P. Using a Fuzzy Decision Tree Ensemble for Tumor Classification from Gene Expression Data. DOI: 10.5220/0004658203200331 In Proceedings of the 5th International Joint Conference on Computational Intelligence (SCA-2013), pages 320-331 ISBN: 978-989-8565-77-8 Copyright c 2013 SCITEPRESS (Science and Technology Publications, Lda.) Data obtained from cancer related gene expression studies typically consists of expression level measurements of thousands of genes. This complexity calls for data analysis methodologies that will efficiently aid in extracting relevant biological information. Previous gene expression analysis work emphasizes clustering techniques (nonsupervised classification), which aim at partitioning the set of genes into subsets that are expressed similarly across different conditions. On the other hand, supervised classification techniques (also called class prediction or class discrimination) with the aim to assign examples to predefined categories, (Golub et al., 1999; DiazUriarte and de Andrés, 2006; Nitsch et al., 2010). The objectives of supervised classification techniques are: 1) to build accurate classifiers that enable the reliable discrimination between different cancer classes, 2) to identify biomarkers of diseases, i.e. a small set of genes that leads to the correct discrimination between different cancer states. This second purpose of supervised classification can be achieved by classifiers that provide understandable results and indicate which genes contribute to the discrimination. Following this line, in this paper the goal is to apply two techniques to classify and select features to tumor datasets in order to carry out an analysis of these datasets and to obtain the information that provide understandable results. We use the Fuzzy Random Forest method (FRF) proposed in (Bonissone et al., 2010; Cadenas et al., 2012a) and the Feature Selection Fuzzy Random Forest method (FRF-fs) proposed in (Cadenas et al., 2013). This paper is organized as follows. First, in Section 2 some techniques applied to gene expression data reported in literature are briefly described. Next, in Section 3, the applied methods are described. Then, in Section 4 we perform an analysis of two tumor datasets using these methods. Finally, in Section 5 remarks and conclusions are presented. 2 MACHINE LEARNING AND GENE EXPRESSION DATA In this section, we describe some of the machine learning techniques used for the management of gene expression data. 2.1 Cluster Analysis based Techniques Clustering is one of the primary approaches to analyze such large amount of data to discover the groups of co-expressed genes. In (Mukhopadhyaya and Maulikb, 2009) an attempt to improve a fuzzy clustering solution by using SVM classifier is presented. In this regard, two fuzzy clustering algorithm, VGA and IFCM have been used. In (Alon et al., 1999) a clustering algorithm to organize the data in a binary tree is used. The algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues. Coregulated families of genes clustered together. Clustering also separated cancerous from noncancerous tissue. In (Golub et al., 1999) a SOM to divide the leukemia examples into cluster is used. First, they applied a two-cluster SOM to automatically discovering the two types of leukemia. Next, they applied a four-cluster SOM. They subsequently obtained immunophenotype data on the examples and found that the four classes largely corresponded to AML, Tlineage ALL, B-lineage ALL, and B-lineage ALL, respectively. The four-cluster SOM thus divided the examples along another key biological distinction. In (Ben-Dor et al., 2000) a clustering based classifier is built. The clustering algorithm on which the classifier is constructed is the CAST algorithm that takes as input a threshold parameter t, which controls the granularity of the resulting cluster structure, and a similarity measure between the tissues. To classify a example they cluster the training data and example, maximizing compatibility to the labeling of the training data. Then they examine the labels of all elements of the cluster the example belongs to and use a simple majority rule to determine the unknown label. 2.2 Techniques for Feature Selection and Supervised Classification Discovering novel disease genes is still challenging for constitutional genetic diseases (a disease involving the entire body or having a widespread array of symptoms) for which no prior knowledge is available. Performing genetic studies frequently result in large lists of candidate genes of which only few can be followed up for further investigation. Gene prioritization establishes the ranking of candidate genes based on their relevance with respect to a biological process of interest, from which the most promising genes can be selected for further analysis, (Nitsch et al., 2010). This is a special case of feature selection, a wellknown problem in machine learning. In (Golub et al., 1999) a procedure that uses a fixed subset of “informative genes” is developed. These “informative genes” are chosen based on their correlation with the class distinction. Using a Fuzzy Decision Tree Ensemble for Tumor Classification from Gene Expression Data