Evidence Intelligence
Explore our interconnected AI platform
CrisPRO.ai
Research Use Only

Evidence Intelligence Platform

Transform raw research findings into structured, actionable evidence with AI-powered confidence scoring, multi-dimensional analysis, and population context.

95.7% ClinVar AUROC
53,210 variants validated
Real-time variant interpretation

Evidence Intelligence

Automated evidence tiering with confidence scoring and full provenance tracking

95.7% ClinVar AUROC

S/P/E Fusion

Integrates Structure, Phenotype, and Expression data for comprehensive variant analysis

95.0% BRCA AUROC

Data Lab

Interactive study browser with real-time therapeutic pipeline integration

Rapid cohort extraction

SAE Intelligence

Interpretable AI that explains predictions with biological context and feature attribution

Transparent AI explanations

Cohort Context

Population-level insights that enhance individual patient treatment decisions

Population-level insights

Unified Workflow

Seamlessly connected tools that accelerate research from hypothesis to validation

End-to-end R&D acceleration

Interconnected Intelligence

Evidence → S/P/E Fusion

Evidence confidence scores enhance multi-dimensional variant predictions

S/P/E Fusion → Data Lab

Fusion results feed into study analysis for comprehensive validation

Data Lab → SAE Intelligence

Study findings inform AI feature attribution and disruption scoring

SAE → Cohort Context

AI explanations provide biological context for population stratification

Evidence Intelligence: Confidence, Tiers, Badges, Citations

Make decisions easier by showing exactly how strong the evidence is — and why — in one view you can trust and share.

95.7% ClinVar AUROC
53,210 variants validated
Real-time variant interpretation

Evidence Intelligence Engine

See how raw findings transform into structured evidence

What This Demonstrates:

  • • How raw research findings are automatically tiered by confidence and evidence strength
  • • The difference between clinical, preclinical, and computational evidence types
  • • How citation count and study quality affect overall confidence scoring
  • • Why transparent evidence assessment accelerates research decision-making

S/P/E Fusion: Unified Variant Interpretation

Go beyond single-metric scores to a unified, biologically-grounded conclusion. Transform disparate data points into a single, coherent evidence story.

95.0% BRCA AUROC
95.7% ClinVar validation
Multi-dimensional analysis

S/P/E Fusion Engine

See how Structure, Phenotype, and Expression data combine for variant assessment

Select a variant for S/P/E analysis:

What This Demonstrates:

  • • How structural predictions, phenotype data, and expression patterns are integrated
  • • Why multi-dimensional analysis provides more accurate variant interpretation
  • • How fusion scoring combines evidence from different biological layers
  • • The clinical relevance of comprehensive variant assessment for therapeutic decisions

In-Silico Data Lab

Give researchers a reliable data lane: discover a study, extract it with guardrails, add labels, run a quick benchmark, and export artifacts — all with provenance.

Rapid cohort extraction
10x faster study analysis
Full provenance tracking

Data Lab Interactive Browser

Explore genomic datasets and therapeutic pipelines in real-time

What This Demonstrates:

  • • How researchers can instantly access and explore large-scale genomic datasets
  • • Real-time therapeutic pipeline insights integrated with study data
  • • The power of collaborative research platforms for accelerating discovery
  • • How data accessibility levels balance open science with intellectual property

SAE Intelligence: Interpretable Genomic Features

Transform black-box AI into transparent, biologically-grounded explanations with interpretable feature analysis.

Interpretable AI
Feature attribution
Transparent predictions

Feature Overlay Visualization

Toggle Features:

Genomic Sequence (43044290-43044450):

Feature Types:

Exon
Intron
TFBS
Structure
Motif

Disruption Scores (ΔLL)

4
Features
2
High Impact
25.6
Total ΔLL

Exon Boundary

High Impact

-12.5
ΔLL

TF Motif (AP-1)

High Impact

-8.2
ΔLL

Secondary Structure

Medium Impact

-3.1
ΔLL

Splice Site

Low Impact

-1.8
ΔLL

Key Insight:

The ΔLL (Delta Log-Likelihood) score quantifies how much a variant disrupts each biological feature. Negative values indicate disruption, with more negative values showing greater impact.

Prompt Safety Checker

2
Check Types
0
Safe Patterns
1
High Risk

Key Benefits:

  • • Prevents pathological inputs that could generate junk outputs
  • • Flags low-complexity repeats and ambiguous sequences
  • • Improves reliability of generative AI demonstrations
  • • Provides clear suggestions for sequence improvement

Activation Steering (Roadmap)

Roadmap Feature

Overall Progress

46% Complete

AP-1 Binding Sites

Transcription factor binding motifs

TFBS
Current:
0.3

Open Chromatin

Accessible chromatin regions

Chromatin
Current:
0.5

Alpha Helix

Protein secondary structure

Structure
Current:
0.2

Roadmap Feature

Activation steering is currently in development. This demo shows the planned interface for controlling feature activations during generation, with compute-aware beam search and predictable quality scaling.

Planned Benefits:

  • • Steer generation towards desired biological features
  • • Predictable quality scaling with transparent controls
  • • Compute-aware beam search for efficient generation
  • • Auditable design process with clear provenance

Cohort Context

Add small, trustworthy cohort snippets to ground your in-silico results without slowing decisions.

Cohort Context Engine

See how population-level insights enhance individual patient decisions

Select a cohort for contextual analysis:

What This Demonstrates:

  • • How population-level genetic and clinical data informs individual treatment decisions
  • • The importance of demographic and ethnic considerations in precision medicine
  • • How cohort context reduces uncertainty in variant interpretation and therapy selection
  • • Why representative population data is critical for equitable healthcare outcomes