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The development of data mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. Cancer classification has improved over the past 20 years; there has been no general approach for identifying new cancer classes or for assigning tumors to known classes (class prediction). Most proposed cancer classification methods...
An adaptive fuzzy c-means (AFCM) clustering based algorithm was developed and applied to the segmentation and classification of multi-color fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means (FCM) clustering algorithm by introducing a gain field, which...
Microarray data has been widely used to predict different disease condition. But the problem has been the high dimensionality of microarray data, because of very few samples compared to a huge number of genes. To tackle this necessity we have developed EVOL Optimer (Evolutionary Optimization). In our method we used both filter and wrapper based approach for gene selection. The original subsets are...
Feature selection continues to grow in importance in many areas of science and engineering, as large datasets become increasingly common. In particular, bioscience and medical datasets routinely contain several thousands of features. For effective data mining in such datasets, tools are required that can reliably distinguish the most relevant features. The latter is a useful goal in itself (e.g. such...
The risk of common diseases is likely determined by single nucleotide polymorphisms (SNPs). However, due to the tremendous number of candidate SNPs, there is a clear need to genotyping by selecting only a subset of all SNPs that are highly associated with a specific disease. In this paper, a new algorithm which is based on a hybrid of binary Particle Swarm Optimization (BPSO) and estimation distribution...
DNA microarray data is a challenging issue for machine learning researchers due to the high number of gene expression contained and the small samples sizes. To deal with this problem, feature selection methods, such as filters and wrappers, are typically applied to reduce the dimensionality. In this work, we apply a filter method before the classification and include a discretization step. The results...
The role of micro array expression data in cancer diagnosis is very significant. Mining for useful information from such micro array data consisting of thousands of genes and a small number of samples is often a tough task. Colon cancer is the second most common cause of cancer mortality in Western countries. According to the WHO 2006 report colorectal cancer causes 655,000 deaths worldwide per year...
The cancer classification is a major and important study field in the medical research, and DNA microarrays have been proved to provide useful and great information for cancer classification at molecular level, compared with traditional clinical or histopathological information. Bio-markers from microarray gene expression analysis have developed to a new approach for cancer classification but still...
Gene selection is an important issue for cancer classification based on gene expression profiles. Filter and wrapper approaches are used widely for gene selection, where the former is hard to measure the relationship between genes and the latter requires lots of computation. We present a novel method, called gene boosting, to select relevant gene subsets by integrating filter and wrapper approaches...
The invention of DNA microarray technology has modernized the approach of biology research in such a way that scientists can now measure the expression levels of thousands of genes simultaneously in a single experiment. Although this technology has shifted a new era in molecular classification, interpreting microarray data still remain a challenging issue due to their innate nature of “high dimensional...
In order to provide feasible primer set for performing a polymerase chain reaction (PCR) experiment, many primer design methods have been proposed. However, the majority of these methods require a long time to obtain an optimal solution since quantities of template DNA need to be analyzed, and the designed primer sets usually do not provide a specific PCR product size. Evolutionary computation has...
SVM(Support VectorMachine) is used to predict the susceptibility to Chronic Hepatitis from SNP(single nucleotide polymorphism) data. SVM is trained to predict the susceptibility using SNPs. SVM is able to distinguish Hepatitis between normal and Chronic Hepatitis with an accuracy of 75.61% which are much better than random guessing. With more SNPs and other features, SVM prediction using SNP data...
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