Predict potential prognostic or diagnostic biomarkers using ML-ready omics samples on Polly.
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.
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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.
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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.
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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.
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Analyze expression patterns, disease association and relevance in clinical settings with well annotated and harmonized data.
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Filter out false positives and unviable results by cross-validation and comparative studies with relevant public datasets.
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Fast-track biomarker
identification projects by 75% using harmonized multi-omics
samples on Polly.
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