NDx
PRECISION NEUROLOGY

AI-enabled multi-omic
blood tests for neurodegeneration

We help pharma enroll the right patients
and bring successful therapies to patients.

SEPT 2026
The Problem

The Measurement Gap in Neurodegenerative Diseases

— hampers drug development, clinical monitoring, and ultimately patient care.

Diagnosis lags behind
>50M
living with dementiaWHO
3 in 4
cases are never diagnosedADI
Global cost of dementia: $1.3T a yearWHO
Current tests are expensive / invasive / limited
~$5,000
per PET scanUSC — or an invasive spinal tap. Blood tests read Alzheimer's only.FDA
A patient entering a PET scanner
PET
A lumbar puncture performed through a sterile drape
Spinal tap
A hand holding several blood collection tubes
Blood test
Pharma suffers in drug development
~70%
of trial candidates screen-fail — then 12–18 months to see if the drug worksAlz&Dem
Images via Wikimedia Commons — PET: U.S. Navy, public domain · LP: Dragondefuego1976, CC BY-SA 4.0 · Tubes: Tannim101, CC BY 3.0
The solution

Bridging the measurement gap with
AI-powered multi-omic blood tests

Biological foundation
Multi-omic data
AI fusion
Niche market: pharma clinical trials
PLASMA
Neurodegeneration

Dying brain cells shed molecular signals

cfDNA 5mC

Reflects real-time molecular signals and disease state

Autoantibody profile

Profiles personalized immune responses over the longer term

AI-enabled multi-omic integration

Integrates multi-omic signals with clinical and biological knowledge to identify biomarkers

Initial focus
Molecular patient stratification
Future expansion
Progression & treatment response monitoring
Built for scalable, AI-driven biomarker development.
AI roadmap

From research assistant to autonomous AI scientist

An AI-native path to blood diagnostics for neurology.

Build phases
Phase 1 · In development
AI Research Assistant

Scientists guide the agent step by step

Key focus
Data engine
Pipeline automation
Deliverable
Agentic biomarker analysis and development pipeline
Phase 2 · Planned
AI Research Collaborator

Contributes its own judgement alongside the team

Key focus
Benchmarking & evaluation
Classical ML models
Deliverable
Biomarker benchmark + classical ML models
Phase 3 · Long-term vision
Autonomous AI Scientist

Trustworthy judgements, scientists sign off

Key focus
Recursive self-improvement
Blood foundation model
Deliverable
Recursive self-improvement loop
Our current state

Human proof-of-concept completed,
ready to scale up

50
patients
completed 2-year pilot
3
diseases analysed
AD · PD · CBS
2
orthogonal
assay platforms
Multi-omic pilot datasets generated
Human plasma data across neurodegenerative disease cohorts
Core multi-omic workflows established
AI-assisted biomarker discovery pipeline established
Stanford biobank collaborations in place
Stanford OTL invention disclosure filed
Signal extraction is demonstrated in human plasma in three diseases. Next: paired, larger-scale and longitudinal validation across AD, PD and ALS.
Next stage

Planned to build the data foundation
for AI-driven diagnostics

Scale high-quality paired measurements while establishing clinically relevant biomarker value.

Month 6 · target
~320
Initial paired plasma samples
Month 18 · target
2,000+
Total paired plasma samples
Per plasma sample
2assays
cfDNA 5mC + autoantibody profiling
Initial disease coverage
Alzheimer's Parkinson's ALS Healthy controls
Three validation objectives
01 Multimodal value

Complementary biology, patient heterogeneity and use-case utility

02 Longitudinal relevance

Biomarker trajectories linked to clinical change; reproducibility & QC

03 Translational readiness

Candidate panel, locked AI model and pilot-ready assay / report

Target: longitudinally informative clinical annotations; final cohort mix and repeat-sampling allocation to be defined with partners.
Illustrative pharma use case

A screening, monitoring platform for pharma

1 · Pre-screening
Who should enter the trial?

Accessible, rapid, affordable blood test to screen patients for clinical trials.

1,000
candidates
NDx blood screen
all 1,000 candidates
~300
to PET

PET only confirms the shortlist.

PET · today ~$5,000/scan
NDx · modeled ~$950/sample
PET spend drops from 1,000 scans to ~300
Target turnaround: ~2 weeks
2 · Longitudinal monitoring
Is the drug working?

Objective, blood-based molecular monitoring of treatment effect.

PET + CSF ~18 mo
NDx · target 6–12 wks
~15 mo
modeled earlier trial decision
~60%
modeled reduction in test costs
Goal: earlier molecular treatment-effect signal, for faster go / no-go
Illustrative scenario: per-sample pricing, turnaround and savings are NDx models on stated assumptions, not measured product performance.
Market

A big, open market with high growth

TAM $12.8B SAM $5.05B SOM $1.65B per year · circle areas to scale · SOM ≈ 30% of SAM in 5 years
TAM · $12.8B / yr
All brain-disease diagnosis and monitoring
SAM · $5.05B / yr
Neurodegenerative disease specifically — AD, PD, ALS and related
SOM · $1.65B / yr
Our 5-year goal as the category leader — ~30% of SAM
The category just opened — FDA cleared the first Alzheimer's blood test in May 2025; Labcorp launched it nationwide that August.FDAFMI
And it compounds — blood-based AD biomarkers go $169M (2025) → $530M (2033), a 3.1× move.FMI
Capital is following — Gates is funding blood-based Alzheimer's diagnostics and calling for routine screening from age 60.Gates Notes

TAM is the global annual brain-disease diagnosis and monitoring market; SAM is the neurodegenerative-disease segment; the ~30% 5-year share is an NDx assumption · growth FMI · clearance FDA, May 2025 · demand signal Fortune, STAT.

Business model · go-to-market

Pharma pays today, the data compounds

An AI-native biomarker engine where commercial adoption, proprietary data and validation reinforce each other.

The NDx flywheel
NDx AI-native biomarkers 1 2 3 4 Pharma contracts Revenue + clinical problems Paired data Longitudinal multi-omics AI learning Validated insights Better products More pharma adoption
01 · Who pays
Pharma & biotech sponsors

Study contracts + per-sample analytical fees

02 · How we scale
Trials → programs → indications

Reusable assays, AI models and sponsor relationships

03 · What compounds
Proprietary data + validated pan-ND AI

Strengthened by pharma partnerships and repeated learning

Go-to-market · land & expand
01
Retrospective studies

Paid studies on existing trial samples

02
Prospective trials

Longitudinal monitoring & validation

03
Portfolio expansion

More programs, sponsors & indications

Longer-term opportunity: companion diagnostics and clinical monitoring.
Competitive landscape & moat

Biology-guided AI, built around pharma needs

Differentiation today: signal selection + customer focus · Defensibility tomorrow: data + validation + workflow integration

Strategic positioning
CUSTOMER FOCUS Pharma customer-led Technology / model-led Broad data integration Biology-guided signal selection BIOLOGICAL MEASUREMENT STRATEGY PET imaging CSF biomarkers Emerging blood tests NeuroDex Circular Genomics Valinor Network Bio Prima Mente Durin NDx
Illustrative positioning: diamonds = test modalities; dots = companies. Not a performance ranking.
The moat we build
01
Biological insight

Purposeful pairing of cfDNA methylation and autoantibody signals

02
Proprietary learning data

Matched longitudinal profiles connected to disease trajectories

03
Pharma integration

Evidence for stratification, progression and trial workflows

Better data → better models → stronger clinical evidence
Hypothesis today. A validated, compounding advantage over time.
Team

Built to take biomarkers from
discovery to clinics

Wet lab, computational biology and ML product — with the clinical access to run the study.

Ze Yang
Co-founder & CEO
Ze Yang
Molecular Biology & Multi-omics
Scientist, Stanford University
PhD, Stanford University
Daniel Zheng
Co-founder & CTO
Daniel Zheng
AI/ML & Platform Engineering
Siri AI, Apple
Former CTO, Utopia Studios
Siyu He
Scientific Co-founder & CSO
Siyu He
Computational Biology & Biomarker Discovery
Postdoc, Stanford University
PhD, Columbia University
Kathleen Poston
Clinical Advisor
Kathleen Poston
Clinical Neurology & Biomarkers
Professor of Neurology
Stanford Medicine
NDx
PRECISION NEUROLOGY

Building accessible AI for brain health.

yangze@stanford.edu
SEPT 2026
01 / 13
←→ navigate · O overview · F fullscreen

NDx Pre-Pitch · Overview