Developed on GO category, GoMiner (Ashburner et al. 2000; Zeeberg et al. 2003) is a tool for biological interpretation of 'omic' data – including data from gene expression microarrays.
Here we review the historical development of large-scale mathematical modeling of yeast metabolism and the growing scope and impact of applications of these models in four different areas: as guide for metabolic engineering and strain improvement, as a tool for biological interpretation and discovery, applications of novel computational framework and for evolutionary studies.
This atlas can thus be used as a tool for better interpretation of these vascular phenotypes and as a platform to provide insight into normal mammalian vascular development.
GO has been widely used as a tool for the interpretation of microarray differential gene expression by grouping genes according to mapped GO terms instead of looking at single genes.
In this case, a method in genome scale that dampens the noise, preserves the signal, and provides a tool for biological interpretation is most welcome.
Tympanograms were scored based on Jerger's classification (Jerger 1970), which is a tool for clinical interpretation of middle ear pathology.
LeishCyc provides a systematic approach to organizing the evolving information about Leishmania biochemical networks and is a tool for analysis, interpretation, and visualization of Leishmania Omics data (transcriptomics, proteomics, metabolomics) in the context of metabolic pathways.
This study also demonstrates the usefulness of multivariate statistical analysis in groundwater study as a supplementary tool for interpretation of complex hydrochemical data sets.
The National Institute of Standards and Technology NISTT) Database for the Simulation of Electron Spectra for Surface Analysis (SESSA) is a unique tool for interpretation of experimental data for nanostructured surfaces as well as for experimental design with photoelectron energies between 50 eV and 30 keV.
Age-period-cohort analysis is a helpful tool for interpretation of observed temporal trends in disease rates over time, which attempts to sort out the effects of age, calendar period, and birth cohort on disease rates by fitting statistical regression models.
