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Bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap in small-sample settings. However, its performance can deteriorate in the high-dimensional settings prevalent in Genomic Signal Processing. We propose here a modification of Bolstered error estimation that is based on the principle of Naive Bayes. Rather than attempting to estimate a...
Next generation sequencing (NGS) technology has increasingly become the backbone of transcriptomics analysis, but sequencer error causes biases in the read counts. In this paper we establish a framework for predicting true sequences from NGS data. We formulate this task as a classification problem. We define several features, such as log likelihood ratio of estimated true counts, error probability...
When proposing a new classification scheme, perhaps in the form of a classification rule or feature selection method, modelers in the bioinformatics literature typically report its performance on data sets of interest, such as gene-expression microarrays. These data sets often include thousands of features but a small number of sample points, which increases variability in feature selection and error...
Small sample classifier design has become a major issue in the biological and medical communities, owing to the recent development of high-throughput genomic and proteomic technologies. And as the problem of estimating classifier error is already handicapped by limited available information, it is further compounded by the necessity of reusing training-data for error estimation. Due to the difficulty...
In this paper, block diagonal linear discriminant analysis (BDLDA) is improved and applied to gene expression data. BDLDA is a classification tool with embedded feature selection, that has demonstrated good performance on simulated data. However, by using cross validation in training, BDLDA is time consuming, thus not an appropriate algorithm for gene expression data, which has a large number of features...
Error estimation is fundamental in GSP applications, such as the discovery of biomarkers to classify disease, or the construction of genetic regulatory networks, especially in small sample settings. Braga-Neto and Dougherty proposed a kernel-based technique of error estimation, called bolstered error estimation, which was shown empirically to work well in low-dimensional spaces (Braga-Neto and Dougherty,...
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