RQC
RNA QR code
A binary matrix encoding active and inactive metabolon modules from Pairwise RNA Co-expression Analysis (PRCA). Each code is a systems-level fingerprint of a biological state; more than 80 modules are tracked.
The RNA QR Code Platform · Miami, Florida
Every tumor speaks.
We decode the language.
Transcriptomic biomarkers for drug discovery, clinical trial enrichment and precision oncology. One image per sample. Two scores: how far disease has moved a cell's gene network, and how far an intervention moves it back toward healthy.
Oncology
Drug repurposing
Trial optimization
Personalized Rx
Neuroscience
I. The problem
Clinical trials fail — at enormous cost
Patient heterogeneity dilutes efficacy signals. Single-gene biomarkers miss network-level biology. Pharma needs systems-level patient stratification, and it does not have it.
90%
Drug failure rate in clinical trials.
~10 yr
Average time to FDA approval.
1 in 400
B cells carry dormant EBV in lupus, versus fewer than 1 in 10,000 in healthy controls.
"A drug effective only in a subset of patients may fail a trial entirely if responders are not prospectively identified."
90% figure: Sun et al., Acta Pharmaceutica Sinica B (2022). 1-in-400 figure: Younis et al., Sci Transl Med (2025).
II. The platform
RNA QR codes — a molecular fingerprint
About 20,000 genes are condensed into 80+ functional modules (co-expressed gene groups Dr. Ji calls metabolons). Each sample becomes one visual code — an RNA QR code — and is scored two ways: how broken the network is, and how well an intervention repairs it.
TI
Disruption Index
The fraction of metabolon networks dysregulated versus normal. Higher TI means greater disease disruption. Inside oncology it is read as a tumor index; outside it, as a disruption index.
RI
Restoration Index
The percentage of disrupted modules an intervention returns toward their healthy state. A single score that ranks candidates head-to-head and predicts patient response.
Fig. 1 · How an RNA QR code is read
Transcriptome
~20,000 genes
measured in a tissue sample
Healthy
81 modules
condensed into a 9 × 9 code
Disease
TI = 16 / 81 = 20%
modules the disease has changed
Disease + drug
RI = 9 / 16 = 56%
disrupted modules the drug restores
Illustrative example; real codes track 80+ modules.
01
Pairwise RNA co-expression
Compute r² for all gene pairs across samples. Retain pairs above threshold. Cluster into metabolon modules.
02
Supermetabolon extraction
Identify groups of metabolons whose expression correlates linearly across patients.
03
RNA QR code generation
Active metabolons become dark modules, inactive ones light. The 2-D matrix is a compact fingerprint of network state.
04
TI & RI computation
TI = disrupted ÷ total modules. RI = restored ÷ disrupted. Objective, quantitative, comparable across drugs and patients.
Because the scores run on transcriptome data, approved drugs and natural compounds can be re-scored against diseases that already have public expression data, before new trials are planned. The full method is on the Platform page.
III. Evidence
Early evidence in two cancers
Retrospective · public datasets (GEO, TCGA) · preliminary
Retrospective analyses of public datasets show the scores separate survival groups and quantify drug effect — on data that already exists, with no new patients and no lab costs.
Breast cancer · doxorubicin
Pre-treatment RNA QR codes distinguished longer- from shorter-survival groups in a retrospective look at public data, and the method quantified how many disrupted modules the drug restored.
A single frozen restoration index — one formula, one module definition, code anyone can rerun — is in preparation. Headline percentages are withheld until the method is frozen and preregistered.
Prostate cancer · androgen therapy
Across normal and tumor prostate tissue the method quantified both how much the tumor state disrupted the normal network and how much the therapy restored it.
Final indices await the frozen protocol and an independent rerun, so every number stays reproducible.
Discovery without new patients
These are computational analyses of existing public data. They have not been prospectively validated and are not evidence of clinical efficacy. The next step, now underway, is pre-registered analysis of Epstein–Barr virus (EBV) disease, with success criteria fixed in advance.
Full figures and methods are summarised on the Evidence page.
Retrospective analyses of public datasets (GEO, TCGA). Preliminary; not reviewed by the FDA. Prior results do not guarantee future outcomes.
IV. Compound-agnostic validation
The cell decides. Not the compound's origin.
The Restoration Index asks one question of any intervention: how many of the modules that disease disrupted have returned to their healthy state. The answer comes from the transcriptome itself, in the language of the cell. A pharmaceutical, a botanical extract and a natural compound are read by the same code, scored on the same scale, and pass or fail by the same biology. There is no separate standard for natural compounds and no separate standard for drugs.
i
Same input
The transcriptome of the disease model before and after the intervention, from RNA-seq or microarray.
ii
Same code
The RNA QR code is generated by the same PRCA method regardless of what was applied; the compound never enters the calculation.
iii
Same score
RI = restored ÷ disrupted modules. A drug and a natural compound compared head-to-head on one axis.
Validation is a measurement, not an endorsement. The indices measure network change in transcriptome data; they are not clinical outcomes, and the platform does not diagnose or treat any condition.
V. Platform applications
One method, five uses
The RNA QR code is a platform, not a single-indication assay. Every approved drug can be re-scored.
i
Clinical trial enrichment
Pre-screen patients by RNA QR code and enroll only predicted responders — raising efficacy signal and reducing wasted spend.
ii
Drug efficacy ranking
Apply candidates to models, compute RI for each, and rank by restoration score without waiting for survival endpoints.
iii
Drug repurposing
Compare a candidate's code to known drugs. High similarity suggests similar effect. Screen entire libraries for unexpected agents.
iv
Personalized therapy
Match each patient's tumor code to the drug whose code best counteracts it — lock-and-key precision at the network level.
v
Longitudinal monitoring
Re-generate the code after each cycle, track RI over time, and detect resistance early.
AlphaExpression
The cloud platform
PRCA computation, QR code generation and TI/RI analytics for academic and industry research teams, with an API for CRO and pharma integration.
VI. EBV program
Why Epstein–Barr virus first
Stanford's recent work shows Epstein–Barr virus turning a small share of B cells into network-level drivers of lupus: a cascade our method is built to read.
- About 1 in 400 B cells carry dormant EBV in people with lupus, versus fewer than 1 in 10,000 in healthy controls (Younis et al., Sci Transl Med, 2025).
- The same mechanism is suspected in multiple sclerosis, rheumatoid arthritis, Crohn's disease and related autoimmune conditions.
- EBV-infected B cell lines and lupus cohorts already sit in public repositories, allowing pre-registered analyses without new wet-lab work.
The first three analyses are specified in advance and run on public data only. Details live on the EBV program page.
Pre-registered readout
- EBV-infected vs. normal B cells — TI separates infected from normal B cells.
- Lupus patients over time — TI tracks disease activity on held-out samples.
- Approved-drug screen — about a dozen approved drugs ranked by how well they restore the EBV network signature.
VII. What makes it different
A systems-level reading, in one code
Existing network and genomic tools either lack a unifying visual code, do not provide a drug-response index, or focus on single pathways. The RNA QR code integrates discovery, stratification and monitoring into one system.
Systems-level, not single-gene
Captures network-wide co-expression across 80+ metabolon modules, rather than resting on any individual mutation or protein biomarker.
Quantitative, comparable metrics
TI and RI convert qualitative differences into objective numbers, so candidates are ranked head-to-head and patients scored on one framework.
Cross-disease by construction
Early evidence in breast and prostate cancer; the approach extends to autoimmune and CNS indications and to non-genomic modalities such as EEG and proteomics.
Protected method
A provisional patent (January 2026) covers the PRCA method, RNA QR code generation, the TI/RI metrics, trial enrichment and companion diagnostics.
VIII. Science
Fifty-four years of theory, now measurable
Cell language theory holds that cells communicate using molecules as words, and that genetic information exists in two forms — DNA carrying it in time, RNA expression profiles carrying it in space. The RNA QR code reads that spatial information directly. RNAscopy, Dr. Ji's term, is the clinical practice built on it.
1972
The conformon: a packet of conformational strain carrying both free energy and genetic information.
1982
Rutgers faculty — Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy.
1997
Cell and human languages share a core set of principles (BioSystems 44:17–39).
2012
Molecular Theory of the Living Cell (Springer).
2018
The Cell Language Theory (World Scientific).
2026
RNA QR code system: provisional patent application filed; 54 years (since 1972) of work behind the method.
The full archive of publications, talks and personal writing is maintained at The Human RNA Project, and on the Science page.
IX. Leadership
Scientific founder
Sungchul Ji, PhD
Chief Scientific Officer; scientific founder and inventor. Professor Emeritus, Theoretical Cell Biology, Rutgers University. Originator of the conformon, cell language theory, PRCA methodology and RNA QR codes. Full archive at sungchulji.com.
We are hiring a computational biologist and patent counsel, and advisory seats are open in immunology and biostatistics. Biographies and roles are detailed on the Leadership page.
X. Mission
Research with purpose
- Cut the cost, time and animal suffering required to discover and validate drugs and natural compounds.
- Open a new era of personalized medicine through RNAscopy, a field named by Dr. Sungchul Ji.
- Favour natural compounds where their selectivity, safety and availability are better.
- Find new uses for FDA-approved medicines identified by the method.
Contact
Research partners, collaborators and investors
For scientific collaborations, CRO and pharma partnerships, press and qualified investors.
[email protected]
X @Bioceuticalsai
Investor materials are available to accredited investors on request via the Investors page.