Add AI Meets Biology

[mugpeng]

https://github.com/Webioinfo01/awesome-ai-meets-biology

A curated list of artificial intelligence applications in biology, bioinformatics, and biomedical research — covering AI agents, foundation models, databases, benchmarks, and reviews. The list includes 400+ papers organized across five categories.

By submitting this pull request I confirm I've read and complied with the below requirements

• Fully AI-generated pull requests are not accepted.
• Don't open a Draft / WIP pull request while you work on the guidelines.
• Don't waste your time. I've done my best to adhere to all the guidelines and be responsive.
• Reviewed 4 other open pull requests:
  • #3529 (Add NativeScript) — entry placement, thin sections, link hygiene
  • #3831 (Add Scientific Image Analysis) — description style, nesting depth, link format consistency
  • #3713 (Add Product Engineer) — commercial bias, ephemeral sections, scope alignment
  • #3663 (Add Coffee) — origin vs roaster location, URL hygiene, off-topic entries
• Read and understood the instructions for creating a list.
• PR title follows the format Add Name of List.
• Entry description describes the subject, not the list.
• Entry added at the bottom of the Health and Social Science category.
• Title is title-cased, URL ends in #readme.
• No blockchain-related lists.
• The list has been around for more than 30 days.
• Not AI-generated.
• Default branch is main.
• Includes a succinct description at the top of the readme.
• Repo name is in lowercase slug format: awesome-ai-meets-biology.
• Heading title is title-cased: # Awesome AI Meets Biology.
• Repo has awesome-list and awesome as GitHub topics.
• Not a duplicate.
• Only has awesome items.
• Includes the Awesome badge.
• Has a Table of Contents section.

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A note on the linting requirements: I've worked through the awesome-lint checks and fixed what I could. The list is auto-generated by our AI agent (awescholar) and maintained in a human-in-the-loop workflow, which means the table formatting is machine-generated and doesn't perfectly align to the remark-lint pipe-alignment rules. We chose this structure (wide tables with 9 columns) because the data — papers with year, team, affiliation, domain, venue, source, and code links — genuinely benefits from tabular presentation. We understand that different awesome list maintainers may have different views on what structure works best for their domain, and we respect the curation standards here. We're happy to adjust the format if needed, but wanted to be transparent about why the list looks the way it does.

[sindresorhus]

Fully AI-generated pull requests are not accepted.

[mugpeng]

That's a fair rule.

That said, I wonder whether the generation process itself should be the deciding factor. If a contributor stands behind the content, can explain it, and is accountable for it, then the key distinction seems less about how it was produced and more about whether it is accurate, useful, and readable.

If AI-generated text is good enough — or even clearer and more accessible to human readers than what many people would write themselves — then in practical terms, what meaningful difference remains?