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Multiomics Integration
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Multiomics Integration

Cross-Platform Biological Discovery

We integrate transcriptomics, proteomics, metabolomics, epigenomics, clinical metadata, and phenotypic measurements to discover coordinated disease mechanisms and actionable biomarkers. Our workflows harmonise heterogeneous platforms into a unified analytical framework, then apply network, statistical, and machine-learning methods to surface the signals that matter.

Key Features

  • Cross-Platform Harmonisation & Feature Engineering

    Data normalisation, batch alignment, and feature engineering reconcile heterogeneous platforms into a consistent, analysis-ready matrix.

  • Integrated Clustering & Network Analysis

    Multi-omics factor analysis, correlation networks, and module detection reveal co-regulated biological programmes spanning data layers.

  • Biomarker Discovery

    Statistical and ML-based feature selection identify robust multiomics biomarkers validated across cohorts and data types.

  • Interpretable Models & Validation Strategy

    SHAP values, feature ranking, and pathway-level integration translate complex models into mechanistic hypotheses with clear validation roadmaps.