Who should use the Biomarker Discovery workflow?
Teams or solo builders working on science & healthcare tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Science & Healthcare
Practical execution plan for biomarker discovery with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A validated biomarker panel with a clinical report ready for publication, patent filing, or diagnostic development.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A validated biomarker panel with a clinical report ready for publication, patent filing, or diagnostic development.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Huma (formerly Medopad) to a signed-off study protocol with clear clinical question, design, and sample logistics. Then, you pass the output to a specialized tool to a curated set of qc-passed samples ready for high-throughput profiling. Then, you pass the output to a specialized tool to raw molecular data files from all samples with documented run metrics (e.g., read depth, signal intensity). Then, you pass the output to a specialized tool to a clean, normalized, and batch-corrected feature matrix ready for statistical analysis. Then, you pass the output to scikit-learn to a shortlist of candidate biomarkers with statistical support and cross-validated performance metrics (auc, sensitivity). Then, you pass the output to BERG (BPGbio) to a validated set of biomarkers with biological context and confirmed differential expression in an independent sample set. Finally, Microsoft 365 is used to a validated biomarker panel with a clinical report ready for publication, patent filing, or diagnostic development.
Define Clinical Question & Study Design
A signed-off study protocol with clear clinical question, design, and sample logistics.
Sample Acquisition & Quality Control
A curated set of QC-passed samples ready for high-throughput profiling.
High-Throughput Molecular Profiling
Raw molecular data files from all samples with documented run metrics (e.g., read depth, signal intensity).
Data Preprocessing & Normalization
A clean, normalized, and batch-corrected feature matrix ready for statistical analysis.
Statistical Modeling & Candidate Selection
A shortlist of candidate biomarkers with statistical support and cross-validated performance metrics (AUC, sensitivity).
Biological Validation & Pathway Analysis
A validated set of biomarkers with biological context and confirmed differential expression in an independent sample set.
Clinical Translation & Reporting
A validated biomarker panel with a clinical report ready for publication, patent filing, or diagnostic development.
Collaborate with clinicians and biologists to specify the disease, patient population, and intended use (diagnostic, prognostic, or predictive). Choose a study design (case-control, cohort, or longitudinal) and define sample size, biospecimen types (blood, tissue, urine), and key covariates.
Why Huma (formerly Medopad): Huma supports decentralized clinical trial management and remote patient monitoring, which aligns with defining clinical questions and study design in biomarker discovery.
Procure biospecimens from biobanks or clinical sites, then perform rigorous QC (e.g., RNA integrity number, protein concentration, hemolysis check). Document sample metadata and exclude low-quality samples to avoid confounding.
Apply one or more omics technologies (genomics, transcriptomics, proteomics, metabolomics) to generate raw data. For each platform, follow manufacturer protocols and include technical replicates and blanks to assess noise.
Apply platform-specific pipelines to convert raw signals into quantified features (e.g., gene counts, peptide intensities). Perform normalization (e.g., TMM, quantile, or median-centering) and batch correction (e.g., ComBat, limma) to remove technical variation.
Use univariate (t-test, Wilcoxon, fold-change) and multivariate (LASSO, random forest, logistic regression) methods to identify features significantly associated with the clinical outcome. Apply multiple testing correction (FDR < 0.05) and rank candidates by effect size and reproducibility.
Why scikit-learn: scikit-learn provides classification, regression, and clustering algorithms directly needed for statistical modeling and candidate selection.
Map candidate biomarkers to known biological pathways (KEGG, Reactome) and literature databases (PubMed, DisGeNET) to confirm relevance. Perform orthogonal validation using an independent technique (e.g., ELISA for proteins, qPCR for genes) on a separate cohort.
Why BERG (BPGbio): BERG (BPGbio) offers target identification and validation, directly supporting biological validation and pathway analysis.
Compile findings into a comprehensive report including study design, methods, candidate list, validation results, and proposed clinical utility. Prepare a biomarker panel algorithm (e.g., logistic regression score) and draft a manuscript or regulatory submission (e.g., for IVD development).
Why Microsoft 365: Microsoft 365 provides AI-assisted content creation, real-time data visualization, and automated document governance, suitable for clinical translation and reporting.
§ Before you start
Teams or solo builders working on science & healthcare tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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