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Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may or may not contain binding sites. This poses problems with negative data in a trainable classifier. Here we show that using randomized negative data gives a large boost in classifier performance when compared to the original...
Support Vector Machines (SVMs) ensembles have been widely used to improve classification accuracy in complicated pattern recognition tasks. In this work we propose to apply an ensemble of SVMs coupled with feature-subset selection methods to aleviate the curse of dimensionality associated with expression-based classification of DNA microarray data. We compare the single SVM classifier to SVM ensembles...
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...
Metagenomic studies inherently involve sampling genetic information from an environment potentially containing thousands of distinctly different microbial organisms. This genetic information is sequenced producing many short fragments (<;500 base pair (bp)); each is tentatively a small representative of the DNA coding structure. Any of the fragments may belong to any of the organisms in the sample,...
Base-calling is one of many problems that can be solved using pattern recognition, the act of classifying raw data based on prior or statistical information extracted from the data into various classes. In this paper, we propose a new framework using polynomial classifiers to model electropherogram traces obtained from ABI sequencing machines to perform base-calling. Initially, pre-processing, which...
Side effect machines operate by associating side effects with the states of a finite state machine. The use of side effect machines permits the researcher to leverage information stored in the state transition structure, making machines that might be identical as recognizers behave differently as classifiers. The side effect machines in this study associate a counter with each state so that the number...
Classification is a major task in the gene sequence analysis. Based on the general principle of artificial immune system, this paper first constructed a classifier which inducted antibody-antigen identification, immune colonel reproduction, hypermutation, affinity mature and the network suppression, by simulating how the antigens stimulate the immune network and how the immune network responds. Then,...
Multicategory support vector machines (MC-SVM) are powerful classification systems with excellent performance in a variety of biological classification problems. However, the process of generating models in traditional multicategory support vector machines is very time-consuming, especially for large datasets. In this paper, parallel multicategory support vector machines (PMC-SVM) have been developed...
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