Biomarker discovery pipeline

Start by specifying the disease, tissue type, and phenotype of interest (e.g., early-stage lung cancer vs. healthy). Review existing literature and clinical guidelines to identify known pathways and potential confounders. This step ensures downstream analyses are hypothesis-driven and clinically relevant.

Why MediSearch: MediSearch is specifically designed for medical literature review and clinical trial discovery, directly matching the need for literature search and clinical trial database exploration.

Collect raw or processed genomic, transcriptomic, proteomic, or metabolomic data from public repositories (e.g., GEO, TCGA, PRIDE) or internal studies. Perform quality control, normalization, batch correction, and missing value imputation. Ensure data is in a consistent format for integration.

Why scikit-learn: scikit-learn is a core Python library for data preprocessing and feature selection, directly matching the need for Python-based data curation tools.

Run statistical tests (e.g., t-test, DESeq2, limma) to identify features significantly associated with the phenotype. Apply multiple testing correction (FDR < 0.05) and rank features by effect size. Use domain knowledge to filter to biologically plausible candidates.

Why Causaly: Causaly is explicitly designed for biomarker discovery and disease pathophysiology deciphering, which aligns with differential expression analysis and pathway interpretation.

Combine selected features from different omic layers (e.g., gene expression + methylation) into a unified matrix. Train machine learning classifiers (e.g., random forest, logistic regression, XGBoost) to predict the phenotype. Use cross-validation to evaluate performance and identify the most important features.

Why scikit-learn: scikit-learn provides classification, regression, and clustering algorithms essential for building predictive models from integrated multi-omics data.

Test the top biomarker panel in one or more external, independent datasets (e.g., from different labs or populations). Assess reproducibility using the same model or a simpler threshold-based rule. If performance drops, refine the panel by removing unstable features.

Why ConcertAI: ConcertAI enables cohort discovery and real-world evidence generation, which directly supports validation in independent cohorts using public data repositories.

Compile final biomarker list with effect sizes, confidence intervals, and biological interpretation (e.g., pathway enrichment, druggability). Write a structured report for stakeholders (R&D, clinical team) including recommendations for assay development or clinical trial design. Optionally, create a dashboard for interactive exploration.

Why Tableau AI: Tableau AI provides data analysis and visualization capabilities, directly matching the need for generating reports and actionable insights.

Wrap the entire workflow into a reproducible pipeline using workflow managers (Nextflow, Snakemake) or containerization (Docker, Singularity). Add automated data fetching, QC, and reporting. This enables rapid re-analysis on new datasets or updated cohorts.

Why Flyte: Flyte is designed for ML pipeline orchestration and large-scale batch processing, directly matching the need for workflow automation and scalability.