In multi-omics research, data generation is no longer the bottleneck — interpretation is. Health Evidence, powered by HealthFAX™ AI, closes that gap.
A unified, query-driven platform that synthesizes genomic, transcriptomic, proteomic, functional annotation, and primary literature data into prioritized, citation-linked insight — in seconds.
5+
Integrated omics data layers in a single query
10x
Reduction in cross-database evidence collation time
3
Steps from query to citation-linked biological insight
Zero
Manual data normalization or database toggling required
Interpretation is the new bottleneck
Teams spend significant time navigating PubMed, pathway tools, and bioinformatics databases before meaningful analysis can even begin.
Manual evidence collation across siloed tools
Researchers toggle between disconnected platforms — PubMed, KEGG, UniProt, GEO — stitching together evidence by hand. Slow, error-prone, and unsustainable at scale.
One query. Every data layer. Instant synthesis.
Search by gene, variant, phenotype, or pathway — and receive normalized, ranked, citation-linked evidence across all omics layers in a single interface.
Raw data to structured biological insight — fast
Candidate prioritization accelerated. Literature screening time reduced. Pathway validation tightened. Teams move faster from discovery to decision.
Health Evidence unifies five critical evidence streams into a single, query-driven platform — eliminating the need to navigate separate databases for each data type.
Variant annotations, gene-disease associations, population-level allele frequency data.
Gene expression profiles, differential expression, RNA-seq datasets across tissues and conditions.
Protein function, interaction networks, post-translational modifications and structural data.
Pathway memberships, GO terms, regulatory elements, and tissue-specific expression patterns.
PubMed-linked citations, ranked by relevance and recency, normalized and queryable alongside data.
No data engineering. No cross-database exports. Just a query — and structured, actionable evidence.
Search by gene symbol, variant ID, phenotype, or pathway name. The platform accepts natural language and structured inputs across all omics layers simultaneously.
HealthFAX™ AI normalizes, deduplicates, and ranks evidence from across all connected data sources — weighting by relevance, recency, and biological context.
Receive prioritized, citation-linked, structured biological insight — ready for pathway validation, candidate selection, or downstream analysis. No manual collation required.
Health Evidence removes the manual overhead that slows discovery — so teams spend more time doing science and less time searching for it.
Unified data access replaces hours of cross-database searches with a single structured query.
AI-ranked evidence surfaces the strongest biological signals first — reducing time-to-candidate across discovery programs.
Citation-linked, relevance-ranked results replace manual PubMed triage with precision-filtered evidence.
Teams move directly from data to decision without intermediate collation, normalization, or manual annotation steps.
Health Evidence adapts to the specific demands of each research phase — from early-stage discovery to clinical translation.
From early discovery to late-stage translation, research teams use Health Evidence to move faster and with greater confidence.
Target identification teams use Health Evidence to rapidly assess the multi-omics landscape around candidate genes — surfacing disease associations, expression profiles, and pathway context in minutes rather than days.
Translational scientists cross-reference variant-phenotype associations, protein interaction data, and clinical literature simultaneously — accelerating the path from molecular finding to testable clinical hypothesis.
Clinical genomics teams use prioritized, citation-linked variant evidence to support interpretation workflows — reducing manual curation time and improving consistency across complex cases.
R&D organizations leverage Health Evidence to de-risk pipeline decisions — validating pathway hypotheses, understanding competitive target landscapes, and aligning multi-omics evidence with regulatory-grade documentation needs.
If you're navigating multi-omics interpretation at scale, we'd welcome a brief conversation to explore the fit.