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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...
Ensemble gene (feature) selection is a promising new strategy with many benefits including more stable gene lists and improved classification results. The ensemble portion is achieved through multiple runs of feature selection which are then aggregated into a single result. The critical question is how many iterations of feature selection are appropriate. Too few iterations can make classification...
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...
This study developed a method to identify disease-correlated pathways by integrating copy numbers (CN) and gene expression (GE). To evaluate the correlation between CN and GE, a suitable window size was assessed by simulation. Gene Set Enrichment Analysis (GSEA) was utilized to identify the possible pathways by CN, GE, and their correlations, respectively. Each of those enriched pathways was further...
We identified 90 germline single nucleotide polymorphisms (SNPs) that were informative for discriminative analysis of 9 major cancers among genotyped Framingham Heart Study participants. Support vector machines resulted in the greatest classification performance, which was in the range of 70-100%. The germline SNPs identified are based on DNA from peripheral blood lymphocytes obtained during non-invasive...
In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis....
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...
In the paper we present a brief survey of our results in processing of data from DNA microarray experiments obtained in our collaborative research with M.C. Sklodowska Centre of Oncology. Our experience therefore is strictly connected with problems resulting from cancer diagnosis and therapy but many results have more general issue. We focus our attention on three important stages of microarray data...
The Use of DNA micro-arrays helps scientist detecting several types of cancer at primitive stage. There is a great amount of data that have to be processed when using DNA micro-arrays by computational heavy algorithms. For these two reasons, the great scientific interest of the problem, and the heavy computational load of the algorithm, bioinformatics research community have proposed several approaches,...
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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