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In this article, a time-domain model is proposed to generalize the analysis of complex datasets for nonbrain regions by incorporating artifacts and phase fluctuations.
Gene network analysis of complex datasets, such as DNA microarray results, aims to identify relevant structures that help the understanding of a certain phenotype or condition.
Systems biology refers to the integration and analysis of complex datasets derived from multiple facets of the body's signaling and response pathways (that is, genomics, transcriptomics, proteomics, and metabolomics).
We have implemented FARMS in R statistical language and we showed that FARMS is fast and outcompetes other comparable commonly used approaches, thus providing a new tool for the thorough analysis of complex datasets without the need for massive computational infrastructure.
In addition, the software uses a priori or modeled covariate information (for example G/C content) to represent signal components, which improves detection accuracy especially when the signal-to-noise ratio is low or in analysis of complex datasets (for example DNA copy number amplifications).
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An effective approach to the analysis of these complex datasets is the joined visualization of the disparate biomolecular data on the framework of known biological pathways.
In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well.
While coherent inelastic neutron scattering is typically restricted to single-crystal samples, analysis of the complex datasets obtained on polycrystalline samples remains challenging, even for the simplest of structures.
This author was engaged by the primary author to assist in the analysis of these complex datasets.
The analysis of complex interlinked datasets poses a significant problem for design researchers.
The SEURAT software tool is designed to carry out interactive analysis of complex integrated datasets.
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