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.
Perform feature selection exercises using well-annotated data on Polly.
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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.
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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Biomarker discovery relies on technologies like multi-omics integration, machine learning (ML), statistical modeling, and high-throughput sequencing to identify molecular markers for diseases, aiding in diagnostics and treatment planning.
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Biomarker discovery typically involves the integration of diverse datasets, including genomic, proteomic, and transcriptomic data, followed by statistical analysis and machine learning techniques to identify reliable biomarkers for diagnostics or prognostics.
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Biomarker discovery is crucial for early disease detection, monitoring disease progression, and personalizing treatments. It accelerates the development of targeted therapies and enables precision medicine for improved patient outcomes.
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The biomarker discovery process involves collecting and integrating multi-omics data, identifying potential biomarkers through data analysis and machine learning, validating their clinical relevance, and applying them in diagnostics or therapeutic strategies.
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Elucidata accelerates biomarker discovery by providing an AI-ready platform, Polly, that harmonizes and integrates multi-omics data at scale. It helps researchers to efficiently clean and process diverse datasets, apply machine learning for feature selection, and leverage network analysis for biomarker identification. Polly also provides access to curated public datasets for rapid validation, streamlining the entire discovery process with advanced analytics and clinical metadata integration.
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Challenges in biomarker discovery include data complexity, variability in patient populations, incomplete datasets, and the need for extensive validation to ensure clinical relevance. These hurdles can complicate the process of translating biomarkers into real-world applications.
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