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Structural variation (SV) has been reported to be associated with numerous diseases such as cancer. With the advent of next generation sequencing (NGS) technologies, various types of SV can be potentially identified. We propose a model based clustering approach utilizing a set of features defined for each type of SV event. Our method, termed SVMiner, not only provides a probability score for each...
With the rapid development of high-throughput genotyping technologies, more and more attentions are paid to the disease association study identifying DNA variations that are highly associated with a specific disease. One main challenge for this study is to find the optimal subsets of Single Nucleotide Polymorphisms (SNPs) which are most tightly associated with diseases. Feature selection has become...
Splice sites detection is helpful to the analysis of gene structure and contributes to the prediction of gene products, so it is one of the most important topics in bioinformatics. With the rapid increase of biological data, faster splice sites detecting methods are more appreciated. Markov model and hidden Markov model not only are low-time-cost, but also easy to understand. Besides the detecting...
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