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Several commercial or free software packages have been introduced for data processing, but most of them are vendor dependent.
In [8], a SOM-based method has been introduced for data imputation in incomplete data matrices.
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Probabilistic models were introduced for data reduction.
In this paper, the JSM-2 model was introduced for data compressed collection for sensor network based on distribution compressive sensing theory.
While ordinary one-dimensional clustering assumes expression coherence across all samples as EEM does, a number of biclustering methods have been introduced for expression data analysis to relax this assumption [8] [10].
Recently, machine learning especially sparse learning algorithms have been introduced for GWAS data analysis.
While numerous tools have been introduced for metagenomic data analysis (see Teeling and Glockner (2012) for an overview), only comparatively few tools exist with focus on the specific difficulties arising in the analysis of metaproteomic data.
In order to tackle those problems, a Kalman filter has been introduced for the RSU data.
The Delaunay complex gives a geometric tool to accomplish this task, and most of the new simplicial complexes that have been introduced for the study of data are based on variations of the Delaunay complex.
This acceptance scheme has been introduced for two reasons: the data is very similar to biological samples [ 10] and the structure prediction method is equal to that used at the ITS2 database [ 30] as well as phylogenetic reconstructions [ 25].
Recently, a groupwise image registration (GIR) framework for dynamic PET data has been introduced for motion correction [17, 18].
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