analysis of gene expression variations in lymphoma, using a fuzzy classification model

Introduction: Cancer is a major cause of mortality in the modern world, and one of the most important health problems in societies. During recent years, research on cancer as a system biology disease is focused on molecular differences between cancer cells and healthy cells. Most of the proposed methods for classifying cancer using gene expression data act as black boxes and lack biological interpretability. The goal of this study is to design an interpretable fuzzy model for classifying gene expression data of Lymphoma cancer

Method: In this research, the investigated microarray contained 45 samples of lymphoma. Total number of genes was 4026 samples. At first, we offer a hybrid approach to reduce the data dimension for detecting genes involved in lymphoma cancer. In lymphoma microarray, six out of 4029 genes were selected. Then, a fuzzy interpretable classifier was presented for classification of data. Fuzzy inference was performed using two rules which had the highest scores. Weka3.6.9 software was used to reduce the features and the fuzzy classifier model was implemented in MATLAB R2010a. Results of this study were assessed by two measures of accuracy and precision

Results: In pre-processing stage, in order to classify gene expression data of Lymphoma, six out of 4026 genes were identified as cancer-causing genes, and then the fuzzy classifier model was applied on the obtained data. The accuracy of the results of classification was 96 percent using 10 rules with the highest scores and that using 2 rules with the highest scores was about 98 percent

Conclusion: In the proposed approach, for the first time, a fully fuzzy method named a minimal rule fuzzy classification [MRFC] was introduced for extracting fuzzy rules with biological interpretability and meaning extraction from gene expression data. Among the most outstanding features of this method is the ability of extracting a small set of rules to interpret effective gene expression in cancer patients. Another result of this approach is successfully addressing the problem of disproportion between the number of samples and genes in microarrays with the proposed Filter-Wrapper Feature Selection method [FWFS]

Haplotype block partitioning and tag SNP selection under the perfect phylogeny model

Single Nucleotide Polymorphisms [SNPs] are the most usual form of polymorphism in human genome. Analyses of genetic variations have revealed that individual genomes share common SNP-haplotypes. The particular pattern of these common variations forms a block-like structure on human genome. In this work, we develop a new method based on the Perfect Phylogeny Model to identify haplotype blocks using samples of individual genomes. We introduce a rigorous definition of the quality of the partitioning of haplotypes into blocks and devise a greedy algorithm for finding the proper partitioning in case of perfect and semi-perfect phylogeny. It is shown that the minimum number of tagSNPs in a haplotype block of Perfect Phylogeny can be obtained by a polynomial time algorithm. We compare the performance of our algorithm on haplotype data of human chromosome 21 with other previously developed methods through simulations. The results demonstrate that our algorithm outperforms the conventional implementation of the Four Gamete Test approach which is the only available method for haplotype block partitioning based on Perfect Phylogeny