Uncover Biomarkers More Effectively With Polly

Predict potential prognostic or diagnostic biomarkers using ML-ready omics samples on Polly.

Biomarker
Prediction Needs ML-ready Data

Molecular biomarkers can be powerful for driving efficiency and precision in clinical decision-making. Approaches commonly used to derive them include feature selection exercises, ML, and statistical modeling.  Training these models, however, requires data of a viability level of quality, i.e. clean, linked to critical metadata, and composed of human samples. Faulty models can lead to completely off-the-mark predictions and a material waste of resources.

How Polly Helps?

Uncover Markers Contributing to Diseases

Perform feature selection exercises using well-annotated data on Polly.

Polly’s comprehensive metadata annotations help you efficiently deduce important features being studied in the experiment (for instance, genes, proteins, or metabolites affecting disease progression).

Perform feature subsetting via differential gene expression and principle component analysis.

Prioritize subsetted features using commonly used ML techniques like Random Forest.

Classify Markers According to Their Function

Optimize biomarker classification using clinical metadata information.

Perform complex network analysis to segregate biomarkers according to their function (prognostic, diagnostic, predictive).

Perform complex network analysis on Polly to segregate different types of novel biomarkers.

Validate Identified Markers With Evidence From the Public Domain

Fast-track the validation of identified biomarkers using ML-ready, public datasets on Polly.

Validate the detected markers' credibility by comparing your rsults with published studies on related biomarkers.

Evaluate biomarkers for sensitivity, specificity, and clinical utility through rigorous statistical analysis.

The Polly Difference

Use Deeply Annotated Data

Analyze expression patterns, disease association and relevance in clinical settings with well annotated and harmonized data.

Rapidly Validate Biomarkers

Filter out false positives and unviable results by cross-validation and comparative studies with relevant public datasets.

Accelerate Time to Milestone

Fast-track biomarker identification projects by 75% using harmonized multi-omics samples on Polly.

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Case studies

Elucidata x Hookipa: 7x Faster Insights In Translational Research With Polly

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