From biological state
to a testable hypothesis.

A research question begins with the biology, the measurements, and their context.

Our diverse and growing proprietary biological data foundation is complemented by permissioned partner data and selected public research. The data available for a project determines which questions can be investigated.

Measurements

RNA profiles and complementary molecular evidence.

Study context

Disease state, tissue, treatment, and time points, where available.

Working output

A defined biological state, research question, and evidence base.

Data access, permitted use, and reuse are scoped to each project.

Investigate the regulatory programs associated with biological response.

Eigen-1 is being developed to represent transcriptomic states and investigate how regulatory programs relate to disease, response, and perturbation. Its outputs become hypotheses for mechanistic examination.

Input

Transcriptomic measurements and the biological question.

Research focus

Biological state and candidate regulatory programs.

Working output

Model-derived hypotheses for evidence review and testing.

Model inference is one step in a broader scientific workflow.

Connect a hypothesis to the molecular relationships and evidence around it.

EigenField connects genes, transcripts, proteins, pathways, and supporting evidence. This context helps researchers examine possible mechanisms, compare explanations, and identify the uncertainty that needs to be resolved.

Input

A biological hypothesis and relevant molecular relationships.

Evidence review

Mechanistic context, supporting findings, and unresolved questions.

Working output

An inspectable explanation and the evidence needed for the next decision.

The quality and relevance of the evidence shape the interpretation.

Define the experiments and decision criteria that could support a hypothesis.

Scientific review examines assumptions, alternative explanations, and the evidence gaps that matter. In Foundry, this informs target qualification, asset evaluation, and a program-specific validation plan.

Review

Mechanistic coherence, evidence quality, and uncertainty.

Experiment design

The model system, readouts, and criteria needed for a useful test.

Working output

A validation plan and a clearer decision about what should advance.

Computational hypotheses progress through experimental validation before supporting program-specific claims.