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Understanding the nature of many diseases, including cancer, requires locating somatically acquired rearrangements corresponding to large-scale chromosomal aberrations. Computational methods to detect inter-chromosomal rearrangements based on next-generation sequencing platforms face the big challenge of accurately predicting the location of sites spanned by a typically small number of reads, while...
Microarray gene expression data comes as a time series, where the expression level of a gene is recorded at specific time points. Comparing the time series produced by two genes can give us information about the regulatory or inhibitory relationship between the genes. We present a Dynamic Programming (DP) method to compare gene expression data using geometric similarity. We aim to detect similarities...
Microarray gene expression data comes as a time series, where the expression level of a gene is recorded at specific time points. Comparing the time series produced by two genes can give us information about the regulatory or inhibitory relationship between the genes. We present a Dynamic Programming (DP) method to compare gene expression data using geometric similarity. We aim to detect similarities...
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