Expert-grade therapy response prediction using CrisPRO.ai genome-scale language model. We quantify mutation disruption in critical cancer pathways (RAS/MAPK and TP53) to predict patient sensitivity vs resistance — with live, transcript-aware scoring and strict data hygiene.
Rigorous validation across multiple datasets demonstrates our platform's accuracy and reliability
Overall accuracy across coding/non-coding SNVs and non-SNVs
Exonic/intronic splice prediction accuracy (~82.5–82.6%)
AUROC 0.94, AUPRC 0.84 — oncology benchmark
Real-world insights from Multiple Myeloma research applications
Validated findings from Multiple Myeloma research applications
Will-It-Work-For-Me confidence range for BRAF V600E variants in MM research applications
Efficacy score range (0.17-0.26) for MM variants with consistent performance
Fusion profile used only when AlphaMissense coverage exists - deterministic approach
Real-world MM research shows consistent confidence patterns and efficacy ranges, with fusion coverage providing comprehensive variant analysis.
Live genome-scale language model scoring with transcript-aware, multi-scale analysis
Delta score ≤ -3 indicates high functional disruption and resistance risk
Optimal genomic context window (8,192 nt) for signal-to-noise balance
Estimated consistency across 1k/2k/4k/8k windows for high-confidence calls
RAS/MAPK and TP53 pathway disruption quantification for therapy response prediction
Estimated coverage of KRAS/NRAS/BRAF variants in pathway aggregation
Estimated frequency of TP53 alterations as cooperating hits in MM
Estimated sensitivity vs resistance prediction accuracy in validation cohort
Multiple Myeloma follows a two-hit model with driver and cooperating alterations
Estimated frequency of MAPK pathway activation (BRAF/NRAS/KRAS)
Estimated frequency of TP53/17p alterations as cooperating hits
Estimated frequency of MYC amplification as cooperating alteration
Multiple Myeloma follows a clear two-hit model with MAPK pathway activation as the primary driver, often cooperating with TP53/17p alterations.
Efficiency gains in clinical trial matching for MM patients
Reduction from 50+ to ~5-12 relevant trials
Minutes to generate trial shortlist
Clinical trial matching efficiency dramatically improves with AI-powered shortlisting, reducing manual review time from hours to minutes.
Efficiency gains in clinical trial matching for MM patients
Reduction from 50+ to ~5-12 relevant trials with AI-powered shortlisting
Minutes to generate trial shortlist with Likely/Potential/Unlikely labels
Clinical trial matching efficiency dramatically improves with AI-powered shortlisting, reducing manual review time from hours to minutes.
Expert-grade CrisPRO.ai genome-scale language model with strict data hygiene and transparent error handling
Strict data hygiene with allele and coordinate validation
Fetch and center genomic window from Ensembl
Live genome-scale language model scoring
Map delta scores to functional impact levels
Sum impacts into RAS/MAPK and TP53 pathways
Structured results with full provenance
Strict data hygiene with allele and coordinate validation
Hard fail on errors; no mock data generation
Fetch and center genomic window from Ensembl
Optimal signal-to-noise balance for CrisPRO.ai scoring
Live genome-scale language model scoring
Transcript-aware, no canned lookups
Map delta scores to functional impact levels
Clinically relevant thresholds for resistance prediction
Sum impacts into RAS/MAPK and TP53 pathways
Clinically relevant pathway focus for MM
Structured results with full provenance
Complete audit trail and repeatability
Strict data hygiene with allele and coordinate validation
Hard fail on errors; no mock data generation
Fetch and center genomic window from Ensembl
Optimal signal-to-noise balance for CrisPRO.ai scoring
Live genome-scale language model scoring
Transcript-aware, no canned lookups
Map delta scores to functional impact levels
Clinically relevant thresholds for resistance prediction
Sum impacts into RAS/MAPK and TP53 pathways
Clinically relevant pathway focus for MM
Structured results with full provenance
Complete audit trail and repeatability
Live, transcript-aware scoring with strict data hygiene
No canned lookups, real-time genome-scale language model
Fail rather than fabricate; transparent error handling
KRAS/NRAS/BRAF and TP53 pathway focus
Fusion-ready with splice-aware checks and protein models
Comprehensive tools for variant analysis, therapy guidance, and clinical trial matching
Four chips (Function, Regulatory, Essentiality, Chromatin) in plain language. Turn unknowns into readable signals with helper text and thresholds.
S/P/E fusion: Sequence (CrisPRO.ai) + Pathway (burden) + Evidence (ClinVar/literature) into ranked therapy classes with explainable confidence and citations.
Top 3 MM pathways with one-line "why" and contribution bars; links to therapy alignment.
Simple caution chip to plan conservatively. Confidence and sources included (RUO).
Feasibility, access, off-target preview, delivery notes (demo). 1M-token context enables richer prompts.
Fast shortlist with Likely/Potential/Unlikely and a shareable one-pager. Synonym/biomarker-aware search and structured eligibility.
Confidence (0–1) is a certainty hint; Evidence Tier is Supported/Consider/Insufficient.
Badges show strength (Guideline, RCT, ClinVar-Strong, Pathway-Aligned).
Fusion labeled when eligible; Baseline remains deterministic.