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Sequencing errors are a major issue for several next-generation sequencing-based applications such as de novo assembly and single nucleotide polymorphism detection. Several error-correction methods have been developed to improve raw data quality. However, error-correction performance is hard to evaluate because of the lack of a ground truth. In this study, we propose a novel approach which using ERCC...
RNA-seq data analysis pipelines are generally composed of sequence alignment, expression quantification, expression normalization, and differentially expressed gene (DEG) detection. Each step has numerous specific tools or algorithms, so we cannot explore all combinatorial pipelines and provide a comprehensive comparison of pipeline performance. To understand the mechanism of RNA-seq data analysis...
Clinical decision support systems use image processing and machine learning methods to objectively predict cancer in histopathological images. Integral to the development of machine learning classifiers is the ability to generalize from training data to unseen future data. A classification model's ability to accurately predict class label for new unseen data is measured by performance metrics, which...
Mass spectrometry imaging (MSI) is valuable for biomedical applications because it links molecular and morphological information. However, MSI datasets can be very large, and analyzing them to identify important biological patterns is a challenging computational problem. Many types of unsupervised analysis have been applied to MSI data, and in particular, clustering has recently gained attention for...
Aiming at question that low identification precision of time series model system in noise, the ARMA parameters are estimated using a damped sinusoidal model representation of the autocorrelation function of the noise ARMA signal. The AR parameters are obtained directly form the estimates of the damped sinusoidal model parameters with guaranteed stability. The MA parameters are estimated using a correlation...
In the last few years distributed video coding (DVC) has become a new paradigm for video compression where the encoding process needs to be simple. DVC allows for the development of new applications where the computational complexity and the amount of memory inside the video encoder are limited. In this paper, we introduce a new data representation and coding method for DVC systems. The proposed method...
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