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Evolution of data analysis and visualization


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Find out how network mapping can unleash insights in your data.

Combine experimental data with domain knowledge, statistical methods, multivariate analyses and machine learning to create information rich networks.

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Carry out statistical analyses to identify significant relationships or altered variables between groups.

Use parametric and non-parametric methods to test a variety of hypotheses.

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Identify similarities between groups using supervised and non-supervised clustering methods.

Carry out hierarchical, fuzzy, density and other clustering analyses using a variety of distance and linkage methods.

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Use dimensional reduction and projection methods to analyze and visualize multivariate relationships and trends.

Apply algorithms including PCA, MDS, tSNE and other techniques to overview complex multivariate relationships.

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Add domain knowledge using biochemical pathway analysis.

Visualize changes in groups within a metabolic, proteomic and genomic context.

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Choose from over 200 machine learning algorithms.

Easily conduct regression and classification based predictive modeling using robust model validation and feature selection methods.

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Generate complex biochemical and empirical networks.

Use biochemical domain knowledge, structural similarity and regularized correlations to calculate information rich mapped networks.

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Put it all together with dynamic interactive reports.

Combine and overview all analyses results using interactive reporting features.

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Dmitry Grapov

CDS tools are designed from the ground up with metabolomics in mind. Find out how you can connect your data with context.

Dmitry Grapov, PhD- Director of Data Science and Bioinformatics, CDS

Discover how to get to know and love your data.