Many multivariate visualization and classification tools have been developed [ 1, 2], both 'unsupervised' and 'supervised' (discriminating on treatment group).
Based on several multivariate statistical tools reported in the literature for visualization and classification of high dimensional data [5], [9], [17] [19], [29], we use the most well-known methods such as MGD, MDA, PCA, and ANN.
The goal of the Morph Server is to provide visualization and classification of protein movements.
Besides the obvious system development aspect, advanced visualization and classification techniques [ 6– 8] can be used to improve image rendering and extract information of interest.
The SOM is an unsupervised data visualization and classification technique that reduces high-dimensional data to lower, usually 1 or 2, dimensions [ 31].
Construction of PCA plots based on the 40 samples in the BioMark HD™ and OpenArray® sample datasets revealed significant outliers that had to be removed, otherwise they would have prevented visualization and classification of separate groups (data not shown).
In this article, we describe the use of nonlinear hybrid-two-phase (H2P) unsupervised machine learning (ML) methodologies specifically dimension reduction (DR) in conjunction with clustering for the concurrent visualization and classification of biological samples.
High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin.
The "aqp" (algorithms for quantitative pedology) package was designed to support data-driven approaches to common soils-related tasks such as visualization, aggregation, and classification of soil profile collections.
