Ask HN: Could DNA be represented as "an embedding" in an AI model?
The article discusses the possibility of representing DNA as an embedding in an AI model, where each array element corresponds to the order of TCAG chemicals on a DNA strand. This could potentially allow for the modeling of many DNA strands and the prediction of future generations. The author explores two possible approaches to achieving this, including modeling an entire DNA strand as one model and modeling all of DNA to create a 'DNA AI' that can produce any kind of strand.
- ▪The idea involves representing DNA as a numerical array where each element corresponds to the order of TCAG chemicals on a DNA strand.
- ▪Two possible approaches to modeling DNA in an AI model are proposed: modeling an entire DNA strand as one model and modeling all of DNA to create a 'DNA AI'.
- ▪The potential applications of such a model include predicting future states of humans, testing drugs, and inventing new genes or creatures.
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Record
| Original publisher | Ycombinator |
| Canonical URL | https://news.ycombinator.com/item?id=49267234 |
| Publication time | Wed, 12 Aug 2026 02:38:18 +0000 |
| Retrieval time | 2026-08-12T02:45:42.386Z |
| Last seen | 2026-08-12T02:45:43.120Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | cZcLAyVLwKu7 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Where each Array element is a numerical value that corresponds to the order of TCAG chemicals on the crossbar of the helix of a DNA strand?Has anyone modeled many DNA strands in this way?If it was done, what kinds of problems could we solve using AI - assuming we could model any known DNA, and predict out generations?-----I guess there are at least a couple options:1) Model an entire DNA strand as 1 model, with each gene (section of DNA) being a vector. So you'd have, say, a C. elegans model. You could play with that species in an AI sandbox.2) Model all of DNA (the way we model all of language) to end up with a "DNA AI" that can produce any kind of strand (perhaps inventing genes that don't exist, but could).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.