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PROVENANCE

rg78803 edited this page Sep 9, 2026 · 1 revision

AlphaGenome Atlas β€” the two public artifacts this study measures

Study 44 measures a container, not a model. It needs exactly two things, both public, neither requiring an account, an API key or accepting any terms.

1. atlas_service.proto

The service definition for AtlasService, fetched 2026-09-09 from

https://raw.githubusercontent.com/google-deepmind/alphagenome/main/src/alphagenome/protos/atlas_service.proto

Licensed Apache-2.0 by Google DeepMind, as stated in that repository. It is quoted here under that licence and is shipped verbatim so the sentence this study turns on can be read in its own file rather than taken from us.

The sentence, at line 76-77, a comment on field 4 of message DenseVariantScore:

  // N-d array of scores, in row-major order. Values are stored as single
  // precision floats.
  bytes scores = 4;

2. The announced row count

"9 billion single-nucleotide variants", from Google DeepMind's own announcement of AlphaGenome Atlas, 2026-09-08. Carried as REPORTED β€” it is their published figure and we did not count it.

It is also re-derivable from a corpus already in this repository, which is why the study does not rest on their press release. corpus/crispr-clinical/RUN-full-assembly-n32.txt records GRCh38 primary assembly as 3,099,750,718 bases over 194 sequences, measured by our own program over the bytes. Every base admits exactly three alternate letters, so the number of possible single-nucleotide variants is

3,099,750,718 x 3 = 9,299,252,154

which exceeds their announced 9 billion. The study reports both and uses the SMALLER of the two wherever a larger count would make its own finding stronger.

What is NOT here, and why

No Atlas prediction values. Not one. Their terms restrict outputs to non-commercial use and forbid using them to train models; this study needs no output value to reach its result, and takes none. The finding is a property of the container the values are shipped in, and the container is described in a file they published under Apache-2.0.

🧬 CURES β€” read in this order

Each step is the reason the next one exists. Nothing here is medical advice, and no page calls any medicine safe or unsafe.

1 Β· Why an exact safety screen at all

2 Β· The three libraries, which grow rather than close

3 Β· The maps β€” every place a molecule could act, counted

4 Β· One medicine at a time

  • Zilganersen β€” the first treatment for Alexander disease, screened on the real approved sequence
  • A drug an AI designed β€” rentosertib for pulmonary fibrosis, and exactly what our instruments reach
  • CAR-T, halted β€” the verdict a regulator could re-derive
  • N-of-1 antisense β€” the only safety net at a population of one
  • VERVE-102 β€” the off-target lattice a stranger can re-derive
  • PM359 β€” prime editing, certified before anyone is dosed
  • Del-Zota β€” the one safety question that can be made exact

5 Β· What keeps a disease alive, and what moves it

βš–οΈ How to read any page here

πŸ”¬ The method β€” exact against float, domain by domain

The same move every time: take a domain where a floating-point model is the accepted instrument, compute the same quantity in exact integers, and seal the cases where the two render opposite verdicts. The subject under grading is always the instrument, never the phenomenon.

⚑ Fusion β€” the energy case

🌍 The planet, and the sky

πŸ› Markets, money and risk

βš›οΈ Run a court yourself

πŸ“’ Program ledger β€” every study by lifecycle

A study appears here under the state its evidence has earned, and above under the question it answers. The two are different filings of the same work, on purpose.

βœ… LAW FROZEN Β· DATA SEALED

πŸ”΄ LIVE CLAIM β€” standing, not sealed

🌊 CHARTER Β· OPEN β€” the findings, published either way

β˜€οΈπŸŒ‘ Eclipse 2026 β€” Study 01, DATA SEALED

πŸ”¬ Discoveries and flows

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