Oracle: Discriminative AI Engine

Transform genetic uncertainty into actionable intelligence with zero-shot variant impact prediction

Annihilation of Uncertainty

Unlike black-box platforms, CrisPRO.ai shows you exactly what the AI is thinking. Using Sparse Autoencoders (SAEs), we reveal the 32,768 biological features our models learned—from exon boundaries to transcription factor binding sites—without any human annotation.

🧠 32,768 SAE Features

🔍 100% Explainable

✅ FDA-Ready Evidence

Traditional AI: Black Box

chr17:43044295:A>T

"Variant of Unknown Significance"

❌ No explanation • ❌ No biological reasoning • ❌ No trust

Tempus: Black box

Foundation: Lookup only

Most AI: No explanations

CrisPRO.ai: Transparent AI

chr17:43044295:A>T

🧬 f15680:Exon boundary disrupted

⚡ f24278:Frameshift severity

🎯 f1050:Splice site alteration

✅ Pathogenic • ✅ Biological explanation • ✅ Trust

Core API Endpoints

🎯

Predict Variant Impact

Mathematical proof of functional disruption

VALIDATED METRICS:

95.7%ClinVar validation

ValidatedValidated samples

🧬

Predict Gene Essentiality

Achilles' heel identification for therapeutic targeting

VALIDATED METRICS:

ValidatedCross-species range

ValidatedDepMap correlation

⚗️

Predict Protein Functionality Change

Structural and functional impact assessment

VALIDATED METRICS:

ValidatedDMS correlation

ValidatedExperimental validation

Multi-Modal Predictions

🎯

Variant Impact Prediction

95.7% ClinVar AUROC

ClinVar AUROC95.7%

Samples53,210

KEY FEATURES:

•Zero-shot prediction without training
•All variant types (SNV, indel, coding, noncoding)
•State-of-the-art noncoding performance

🧬

Gene Essentiality Analysis

0.82-0.99 AUROC range

AUROC Range0.82-0.99

DepMap Correlation0.73

KEY FEATURES:

•Cross-species gene prediction
•Cancer cell line dependency analysis
•Therapeutic target identification

⚗️

Protein Function Prediction

Strong correlation with DMS data

DMS CorrelationStrong

ValidationExperimental

KEY FEATURES:

•Deep Mutational Scanning correlation
•Protein stability prediction
•Binding affinity assessment

🧪

Chromatin Accessibility

Context-aware regulatory analysis

SAE Features32,768

Applications2

KEY FEATURES:

•32,768 learned biological concepts
•TF binding motif analysis
•Regulatory element identification

✂️

CRISPR Efficacy

Guide RNA optimization

MethodHybrid

Applications2

KEY FEATURES:

•Variant impact simulation
•Empirical prior integration
•Guide RNA design optimization

Scientific Validation

🏆

ClinVar Validation

Gold standard variant database

Coding SNV95.7%

Noncoding SNV95.8%

Total Samples53,210

🎯

BRCA1/2 Validation

Clinical breast cancer variants

Supervised AUROC94.0%

Zero-shot AUROC89.1%

Samples3,893

✂️

Splice Variant Validation

Experimentally validated splice effects

Exonic AUROC82.6%

Intronic AUROC82.5%

Samples4,950

Clinical Use Cases

🎗️

Hereditary Breast Cancer

BRCA1/2 VUS resolution with 95% confidence

WORKFLOW:

1.Input BRCA1/2 variant sequence
2.Oracle predicts pathogenicity
3.Clinical classification (Pathogenic/Benign)
4.Treatment recommendation (PARP inhibitors/surgery)

73%

vus Resolution

91.3%

risk Accuracy

89.1%

brca1 A U R O C

High

clinical Impact

⚡

Oncogene Activation

KRAS G12C, BRAF V600E therapeutic targeting

WORKFLOW:

1.Identify oncogenic mutations
2.Oracle predicts functional impact
3.Essentiality analysis for targeting
4.Therapeutic strategy selection

94.2%

target Accuracy

98.7%

coverage

85%

time Reduction

High

clinical Impact

🎯

Therapeutic Targeting

Gene essentiality analysis for precision medicine

WORKFLOW:

1.Gene expression analysis
2.Oracle predicts essentiality
3.Cancer dependency scoring
4.Therapeutic target prioritization

0.82-0.99

essentiality Range

0.73

dep Map Correlation

species

High

clinical Impact

⚠️

Research Use Only

Oracle predictions are for research purposes only. Not for use in diagnostic procedures or clinical decision-making.

All variant classifications require experimental validation before clinical application.