Target Discovery & Prioritization
Identify and rank novel therapeutic targets through multi-omics integration, the OmniRef knowledge layer, and direction-aware prioritization scoring
The Challenge
Differential Expression Alone Misses Druggable Targets
Thousands of dysregulated genes, most are downstream consequences. Without integration of druggability, essentiality, and clinical evidence, teams waste months pursuing non-actionable targets.
Upregulated and Downregulated Targets Require Different Strategies
Upregulated = direct inhibition; downregulated = synthetic lethality. Most pipelines treat all dysregulated genes identically, missing the mechanistic distinction that determines therapeutic modality.
No Systematic Way to Integrate External Evidence
Druggability, clinical trial, essentiality, disease association, and clinical annotation data live in separate silos. Manual cross-referencing introduces inconsistency and misses targets that score well across multiple dimensions.
Disease Heterogeneity Demands State-Specific Targets
A target essential in one molecular state may be irrelevant in another. Pooled analyses average across heterogeneity, identifying consensus targets that may not be optimal for any individual state.
"We ran differential expression on our cohort and identified thousands of dysregulated genes. Deciding which ones to pursue took our team months of manual literature review, and we still had no confidence in the ranking."
The Helomnix Solution
Multi-omics latent factor analysis links coordinated biological programs to preclinical response profiles, then traces those programs to individual genes. Differential expression — both pooled across high-risk states and within each state — identifies pan-disease and state-specific candidates.
Every candidate gene is annotated through OmniRef, a structured and curated knowledge layer integrating drug-gene interactions, disease-gene associations, gene essentiality profiles, and clinical evidence annotations at genome-wide scale, with explicit versioning, provenance, and auditability.
A direction-aware composite scoring system weights evidence differently depending on whether a target is upregulated (direct inhibition: druggability and essentiality weighted highest) or downregulated (synthetic lethality: essentiality weighted highest). Targets are assigned clinical development tiers from Tier 1 (immediate) through Tier 4 (exploratory).
Unique Differentiator
Direction-aware scoring distinguishes upregulated targets (direct inhibition candidates) from downregulated targets (synthetic lethality candidates), applying different weighting schemes to druggability, essentiality, and clinical evidence. This mechanistic distinction is absent from standard target prioritization approaches.
How It Works
Multi-Omics Factor Analysis
Unsupervised multi-omics integration decomposes patient data into latent factors capturing coordinated biological programs. Regression links each factor to preclinical response profiles, identifying which programs associate with therapeutic sensitivity patterns. Top genes from drug-associated factors are extracted as initial candidates.
Differential Expression & State Resolution
Pooled analysis (all high-risk vs. low-risk states) identifies pan-disease targets. Per-state analysis reveals state-specific vulnerabilities. Both upregulated and downregulated genes are retained with direction annotation.
OmniRef Knowledge Integration & Scoring
Each candidate is annotated against OmniRef's integrated evidence layers: druggability tiers, clinical trial phases, gene essentiality, disease associations, and clinical evidence. Direction-aware composite scores weight dimensions by therapeutic strategy.
Clinical Development Tier Assignment
Targets are assigned to four tiers: Tier 1 (Highest convergent evidence — clinical evidence plus essentiality confirmed), Tier 2 (Near-term — druggable plus essential), Tier 3 (Development — druggable, essentiality uncertain), Tier 4 (Exploratory). Each state receives a mechanism fingerprint with key drivers and actionability profiles.
Real-World Application
Using a multi-omics hematology dataset, Helomnix applied the full target discovery pipeline to identify actionable therapeutic targets across molecularly defined disease states. Latent factor analysis linked biological programs to preclinical response profiles, and targets were scored through OmniRef.
Before
Standard approach: Differential expression identified thousands of dysregulated genes. Manual literature review narrowed to a handful of known targets. A promising candidate (TARGET_X) was flagged as downregulated but dismissed as non-actionable without further context.
After
Helomnix pipeline: Direction-aware scoring reclassified TARGET_X as a high-priority synthetic lethality candidate despite apparent downregulation. Mediation analysis resolved the apparent paradox — the downregulation was explained by disease biology (differentiation state) rather than loss of therapeutic relevance. State-specific analysis revealed distinct mechanism fingerprints across disease subtypes, each with its own ranked target list.
Outcome
The pipeline identified both established targets (confirming known biology) and novel candidates that would have been missed or misinterpreted by standard approaches. State-specific prioritization revealed that no single target list serves all molecular subtypes — each state has distinct high-priority targets and distinct therapeutic strategies.
Value to Your Organization
Target Identification Efficiency
Replace months of manual literature review with a systematic pipeline integrating OmniRef's comprehensive gene evidence profiles. Every scoring decision is transparent and auditable.
Therapeutic Strategy Clarity
Each target annotated with its therapeutic modality — direct inhibition for upregulated, synthetic lethality for downregulated — enabling rational modality selection.
Pipeline De-Risking
Clinical development tier assignment provides a clear framework for portfolio decisions, supporting resource allocation toward targets with the strongest convergent evidence.
Our Methodology
Data Inputs
- Patient multi-omics data (RNA-seq, proteomics, genomics)
- Ex vivo preclinical response profiles
- Disease state annotations or Digital Twin Map classifications
- Clinical outcomes
- Cell line essentiality data (optional)
AI/ML Techniques
- Unsupervised multi-omics latent factor extraction
- Regression linking latent factors to preclinical response profiles
- Pooled and per-state differential expression
- OmniRef knowledge integration (druggability, essentiality, disease associations, clinical evidence)
- Direction-aware composite scoring
- Mediation analysis for resolving paradoxical expression patterns
Deliverables
- Ranked target list with composite scores and clinical development tiers
- Direction annotation with therapeutic strategy
- State-specific mechanism fingerprints with key drivers
- OmniRef evidence summary per target
- Actionability profiles (druggability tier, existing drugs, clinical status)
- Synthetic lethality candidate list
- Paradox resolution analyses
- Structured evidence summaries supporting portfolio discussions
Discuss a Translational Application
We welcome discussions about how this approach can support your translational research.