Solutions

End-to-End Support from Discovery to Development

Solutions

End-to-End Support from Discovery to Development

ANALYTICAL SOLUTIONS

Seven Workflows for Every Research Question

BiRAGAS supports the full spectrum of translational research through intent-classified, modular workflows – from initial causal discovery to counterfactual simulation and regulatory-grade evidence dossiers.

From Discovery to Explanation

Each workflow is a coordinated sequence of validated analytical modules. Workflows can be composed and chained – a Causal Discovery result can feed directly into Intervention Ranking, or a Comparative study can be followed by Counterfactual simulation to test proposed interventions before wet-lab validation.

Find Causal Drivers of Disease

Discovers novel causal drivers of a disease or phenotype from multi-modal data. Ingests expression data, performs quality control and normalisation, constructs a causal graph, applies temporal and genetic constraints, and validates through Mendelian Randomization and perturbation evidence.

→ Ranked gene list · CCS scores · Validated causal graph · Full evidence dossier

Test “Does X Cause Y?”

Validates a specific causal hypothesis by applying targeted graph discovery, temporal validation, and Mendelian Randomization to the nominated relationship, generating a comprehensive evidence card with effect estimates and confidence metrics.

→ Edge evidence card · Effect estimate · Confidence metrics · Contradiction flags

Prioritise Therapeutic Targets

Ranks candidate interventions by actionability. Combines signature reversal scoring — identifying targets whose modulation most effectively shifts disease expression toward a healthy state — with dose-response modelling and causal confidence weighting.

→ Ranked target list · Intervention effects · CCS scores · Dose-response data

Compare Mechanisms Across Cohorts

Identifies shared and unique causal drivers across patient subgroups, disease subtypes, or treatment arms. Each stratum is analysed independently with shared preprocessing, followed by delta analysis that highlights where causal biology diverges between groups.

→ Delta driver report · Shared/unique edge maps · Stratified causal graphs

Simulate “What If” Scenarios

Models predicted biological consequences of unseen perturbations before wet-lab validation. A trained counterfactual model estimates downstream effects of hypothetical genetic or pharmacological interventions, ranked by predicted effect size with uncertainty quantification.

→ Predicted deltas · Uncertainty estimates · Ranked what-if scenarios

Explain Any Causal Claim

Provides complete transparency into why any causal relationship was identified. Returns a full breakdown of supporting evidence streams, effect sizes, data sources, and any contradictions — supporting scientific review, regulatory submissions, and internal audit trails.

→ Evidence breakdown · Contradiction analysis · Provenance report

Four-Factor Arbitration Framework

When statistical methods detect bidirectional correlations, BiRAGAS applies a four-factor arbitration system integrating orthogonal evidence sources in a defined priority hierarchy to enforce biologically coherent directionality.

Genetic variants are immutable sources of causation. If Gene A has a GWAS association and Gene B does not, A → B is enforced. Highest priority — overrides all other factors.

Genes in different cell types cannot interact directly unless via secreted signals. Cell-type deconvolution provides a veto on physically impossible interactions.

Causes precede effects. If Gene A peaks before Gene B in pseudotime or time-series data, A → B is enforced using Granger causality and trajectory analysis.

Strong CRISPR interventional effects indicate causal drivers. Where Average Causal Effect of A substantially exceeds B, direction A → B is enforced.