SCIENTIFIC FOUNDATIONS
Resolve what the evidence actually supports.
Biology-First Intelligence (BFI) is AnnieGuard’s proprietary causal reconstruction methodology for resolving difficult biological questions when heterogeneous evidence remains compatible with multiple explanations. BFI determines what is supportable, what remains unresolved, and what additional evidence would most change the scientific conclusion.
COMPLEX EVIDENCE
Genomic · Molecular · Clinical · Longitudinal · Multimodal
BIOLOGY-FIRST INTELLIGENCE
Supported
Unresolved
Next Evidence
THE R&D PROBLEM
Strong signals can still support the wrong biological conclusion.
Genomic, molecular, clinical, and computational evidence can produce compelling signals while leaving multiple causal interpretations consistent with the data. For R&D teams, that ambiguity affects target selection, biomarker strategy, mechanism assessment, experimental prioritization, and translational decisions.
Prediction can rank possibilities. BFI is designed to determine what the evidence can actually support.
01
RESOLVE
What is supported?
Evaluate competing biological interpretations against the available evidence.
02
PRESERVE
What remains unresolved?
Keep uncertainty and competing explanations explicit when unique identification is not supported.
03
DISCRIMINATE
What evidence matters next?
Identify the evidence gap most relevant to resolving the remaining ambiguity.
Uncertainty is a scientific output.
SCIENTIFIC DIFFERENTIATION
A different question produces a different scientific output.
PREDICTIVE / ASSOCIATIONAL
What predicts or is associated with an outcome?
Output → prediction, association, classification, risk.
CAUSAL INFERENCE
What causal relationship or effect is identifiable under specified assumptions?
Output → causal estimates or intervention-related conclusions.
SYSTEMS BIOLOGY
What pathways, networks, or system-level relationships characterize the biology?
Output → network organization and mechanistic hypotheses.
BIOLOGY-FIRST INTELLIGENCE
What biological architecture can the available evidence actually support?
Output → supportable biological state, unresolved alternatives, mechanistic interpretation, and discriminating evidence.
A plausible explanation is not automatically an identified explanation.
EVIDENCE IN APPLICATION
21-case undifferentiated sarcoma study
BFI was applied across 21 independently frozen cases to test whether heterogeneous molecular evidence converges at a higher biological level—and whether reconstruction generates information beyond exact feature matching.
19 / 19
Molecularly informative cases supported multi-event structural-genome remodeling
18 / 18
Claim-informative cases supported a broad viable dosage-imbalanced / aneuploid state
18 / 19
Generated at least one state-level mechanistic hypothesis beyond exact feature matching
These results are supported by the public case-study artifact.
The cohort did not converge on a universal driver. The first stable convergence emerged at the biological process/state level.
For R&D teams, that changes the question from “Which alteration should we prioritize?” to “Which biological organization does the evidence support?”
VALIDATION
Algorithmic Integrity & Structural Consistency
BFI has completed internal methodological validation across its tested registered execution scope. Validation was designed to assess deterministic execution, root-identification recovery, model/equivalence preservation, and monotonic behavior relative to evidence strength.
STAGE 01
Internal Methodological Validation
Status: Complete
Algorithmic & causal integrity established within the tested registered execution scope.
STAGE 02
Cohort Application Evidence
Status: Demonstrated
Applied across a 21-case undifferentiated sarcoma cohort, demonstrating recurrent higher-order biological organization and reconstruction-derived mechanistic information beyond exact feature matching.
STAGE 03
External Biological / Translational Validation
Status: Partnering stage
Positioned for external biological validation and wet-lab hypothesis testing through structured pharmaceutical R&D collaborations.
PHARMACEUTICAL R&D
Where BFI can inform decisions
Target & Mechanism Evaluation
Determine whether a target or proposed mechanism reflects broader biological organization or a local molecular feature.
Biomarker Strategy
Assess whether a biomarker corresponds to a coherent biological state.
Patient Stratification
Evaluate whether molecularly heterogeneous cases converge on shared disease organization.
Experimental Prioritization
Identify evidence most capable of resolving a consequential biological uncertainty.
Translational Decision Support
Clarify what the current evidence supports before committing additional R&D resources.
The value is not another target list.
It is a better-defined biological decision boundary.
Bring AnnieGuard a difficult target, biomarker, mechanism, cohort, or multimodal evidence question. We’ll determine whether the available evidence supports the conclusion—and where the remaining uncertainty lies.
Defined research engagements · MSA / SOW compatible