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CIMA / Chemical structure-Informed Metabolomics Analysis
A strategy to boost the statistical power of hypothesis testing in metabolomics by incorporating quantitative molecular descriptors for each metabolite. CIMA selects potentially informative summary molecular descriptors and outputs chemical structure-informed false discovery rates. The proposed approach focuses on the general metabolomic hypothesis-testing problem, whereas incorporating structure information into other common metabolomic inference practices, such as multivariate predictive model construction and network inference, is warranted for further research.
Metabox
Includes widely used statistical methods to process and identify keys entities of input experiments, offers different integrative analysis methodologies and provides interactive visualization to facilitate biological interpretations. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. It supports in-depth analysis of metabolomic data by including four analysis modules: data normalization, statistical analysis, network construction and functional analysis.
Elements for Metabolomics
Assists users in analyzing metabolomics experiments using liquid chromatography coupled with mass spectrometry (LC-MS and LC-MS/MS). Elements for Metabolomics is a program that extracts features from the data, and performs a spectral library search to identify the extracted features. This tool allows users to organize, summarize and visualize the identified metabolites across a large number of biological samples. Moreover, it can support complex experiments by combining attributes (metadata) with Mass Spectrometry data.
HiRes / High Resolution spectroscopy
Obsolete
Combines standard nuclear magnetic resonance (NMR) spectral processing functionalities with techniques for multi-spectral dataset analysis. HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. It couples rigorous data pre-processing, artifact removal and identification of metabolic patterns via principal component analysis (PCA).
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