The Helomnix Platform
A structured disease modeling framework that integrates multi-omics data into reusable Digital Twin Maps and interpretable biological states.
Helomnix applies this disease intelligence framework through collaborative analytical engagements with translational research teams.
From multi-omics inputs to structured biological outputs —
supporting biomarker discovery, target prioritization, and program-level biological insight in hematologic diseases.
How the Platform Works
Helomnix structures heterogeneous molecular data into reproducible disease-state models through a biology-grounded analytical framework.
Multi-Omics Integration
Bulk RNA-seq, single-cell data, proteomics, exome sequencing, digital pathology, and clinical annotations are harmonized and integrated at the cohort level.
Digital Twin Map Construction
Latent biological programs are identified and organized into a structured reference map of interpretable disease states — built once and reused across studies and programs.
Cohort Projection & State Assignment
New patient cohorts are mapped onto the reference framework without retraining. Each sample is assigned to its closest biological state for structured comparison across programs.
Structured Biological Outputs
The framework produces state definitions, molecular signatures, pathway context, and evidence-linked reports — supporting biomarker discovery, target prioritization, and translational research decisions.
Delivery model: collaborative programs with clear milestones and reusable disease state maps as the primary deliverable.
Request the Platform Architecture Brief
A technical overview of the Helomnix Digital Twin modeling architecture — covering reference map design, projection logic, and program-level translational integration.
Available upon request for scientific and translational program discussions.
Operational Integration & Governance
Helomnix operates as structured translational infrastructure — with version-locked reference maps, deterministic outputs, and reproducible analytical logic built into every engagement.
Governance Controls
- Version-locked reference maps with stable, documented topology
- Deterministic projection of new cohorts without model retraining
- Separation of state construction and outcome evaluation to prevent analytical leakage
- Transparent state definitions with recorded projection logic and parameters
- Reproducible cohort outputs generated from fixed inputs and versioned artifacts
Program Integration
- Retrospective projection of historical cohorts and trial datasets
- Mechanism-aware stratification workflows for cohort segmentation
- Target and biomarker context analysis across functional states
- Support for prospective enrichment strategy discussions and study design inputs
Helomnix outputs are structured for cross-functional review across translational biology, biomarker strategy, and clinical development teams.
Stable reference structure — consistent cross-cohort comparability.
Core Analytical Engines
Foundational components of the Helomnix disease intelligence framework — built to convert multi-omics data into reusable biological states and reference maps.
Unified Molecular Representations
Integrates bulk RNA-seq, scRNA-seq, proteomics, and exome data into sample-level molecular representations with latent feature extraction
- Multi-modal companion diagnostic development
- Regulatory-ready biomarker discovery with cross-validation
- Latent dimension extraction for advanced patient modeling
Reusable State Map & Cohort Contextualization
Visual 2D topological map of patient molecular profiles. Projects new patients onto the map to identify the most similar digital twins for biological context matching
- Cohort alignment to reference state maps
- Compound-biology relationship characterization across subgroups
- Molecular subtype classification for translational workflows
AI-Powered Compound Profiling
Deep learning framework characterizing preclinical response patterns and compound–state alignment across a broad compound library using multi-omics digital twin representations
- Preclinical response profiling and compound–state alignment across a broad compound library
- Mechanistic alignment characterization to support study design
- Compound–state association mapping for drug-biology alignment
Translational Workflows Enabled by the Platform
Reusable workflows built on Digital Twin Maps — supporting cohort definition, biomarker exploration, and target prioritization across hematology programs.
Multi-Omics Integration
Data fusion engine integrating bulk RNA-seq, scRNA-seq, proteomics, and exome sequencing into unified patient representations across multiple molecular layers.
Patient Molecular Subtyping
Unsupervised clustering identifies molecular subtypes in multiple myeloma (MM) and diffuse large B-cell lymphoma (DLBCL), revealing clinically relevant patient groups based on biological state rather than single markers.
Preclinical Response Profiling
Compound–state association mapping framework that characterizes preclinical response patterns across compound libraries, supporting translational hypothesis generation.
XAI Biomarker Discovery
Explainable AI identifies robust gene signatures with mechanistic interpretability — designed for regulatory-aware biomarker development and companion diagnostic support.
Digital Pathology AI
Deep learning models with attention mechanisms characterize molecular features from H&E slides — enabling virtual molecular profiling of archival cohorts without re-sequencing, and supporting cost-effective companion diagnostic development.
Knowledge Graph Explorer
Integrates gene essentiality evidence, preclinical response profiles, and pathway knowledge for systematic target contextualization and drug repurposing support.
Data Foundation
Curated multi-omics cohorts and proprietary in-vitro disease models designed for reproducible state modeling and cross-program reuse.
Multiple Myeloma Biobank
Longitudinal multi-omics cohort supporting state definition and biomarker contextualization in multiple myeloma.
DLBCL Image-Omics Cohort
Integrated digital pathology and multi-omics for virtual molecular profiling and companion diagnostic development support
AML Multi-Omics Biobank
Longitudinal multi-omics cohort supporting state definition and biomarker contextualization in acute myeloid leukemia.
In-Vitro Plasma Cell Differentiation Model
Proprietary in-vitro model of normal plasma cell differentiation (Memory B-cell to Plasma cell). A functional state interrogation platform for mechanistic validation and perturbation studies — supporting research in myeloma, Waldenström macroglobulinemia, and autoimmunity without animal models.
Data Modalities
Integrated data layers used to build and validate disease state maps.
Single-Cell Transcriptomics
Tumor heterogeneity profiling, microenvironment analysis, rare cell populations
10x Genomics, high-throughput scRNA-seq
Multi-Omics Integration
Bulk RNA + scRNA + Proteomics + Exome + Imaging for comprehensive molecular portraits
Multiple data layers integrated per patient
Gene Essentiality Data
Target validation using gene essentiality evidence from internal screens and curated reference data
Identify druggable dependencies
Clinical Annotations
Survival outcomes, clinical outcome annotations, disease progression with long-term follow-up
Biomarker contextualization
Engage the Helomnix Platform
Helomnix supports translational development programs through structured, reusable Digital Twin modeling. If you are advancing a clinical asset, refining stratification strategy, or exploring a target hypothesis, we can define a scoped analytical engagement.
Initiate a Scientific Discussion