BioTender-max GitHub avatar

ai-bio-conference-papers

BioTender-max

A curated, machine-readable corpus of 3,722 AI × biology papers accepted at ICLR, ICML, and NeurIPS from 2010 to 2026, with structured data, tags, and figures.

Stars

17

7-day growth

No data

Forks

1

Open issues

0

License

NOASSERTION

Last updated

2026-07-07

AI repository intelligence
FR-AI / ANALYSIS

Why it is worth attention

It is the first definitive, openly released, and reproducible index of AI × biology papers across three top ML conferences, with high recall (95.9%) and human-audited topical tags.

Who it is for

  • AI/ML researchers working in biology or computational biology
  • Biologists and bioinformaticians seeking relevant ML literature
  • Graduate students exploring the intersection of AI and life sciences
  • Conference organizers or reviewers mapping research trends

Use cases

  • Systematic literature review and meta-analysis of AI in biology
  • Trend analysis of topic trajectories (e.g., protein, single-cell) over time
  • Building training datasets or benchmarks from curated paper metadata
  • Quick browsing of papers by venue, year, or tag for research planning

Strengths

  • Comprehensive: 3,722 papers across 17 years with 10 topical tags
  • High recall (95.9%) validated against a 315-paper hand-curated truth set
  • Fully reproducible: all data and markdown files regenerable from one JSON file
  • Dual-licensed (MIT + CC BY 4.0) for flexible reuse

Considerations

  • PMLR floor: ICML abstracts before 2020 are truncated, affecting filtering accuracy
  • Only accepted papers included; rejected submissions and workshops are omitted
  • Tag assignment is subjective and based on first-match in per-year markdown files

README quick start

AI × Biology Papers Corpus

The definitive, machine-readable index of AI × Biology papers accepted at ICLR, ICML and NeurIPS.

Browse  ·  Figures  ·  Tags  ·  Data  ·  Load  ·  Methods


Why this exists

🎯 One canonical corpus

Every AI × Biology paper accepted at ICLR / ICML / NeurIPS, from NeurIPS 2010 to ICLR 2026, in one place. 3,722 records, all with abstract, authors and PDF link.

🏷️ Machine-labelled, human-audited

Two-stage keyword filter with 95.9 % recall against a 315-paper hand-curated truth set. Ten topical tags (protein, molecule, single_cell, …) assigned per paper.

📊 Ready to analyse

Ships as structured JSON + CSV plus 33 foldable markdown files you can browse on GitHub. Reproducible: the entire tree regenerates from one JSON.


🚀 Quick start

git clone https://github.com/biotender/ai-bio-conference-papers.git
cd ai-bio-conference-papers

# Browse in your editor — every paper is a foldable  card
open papers/ICLR/2026.md

# Or load the data
python -c "import json; papers = json.load(open('data/papers_master.json')); print(len(papers), 'papers')"

📈 At a glance

Corpus overview — 3,722 papers, 2010 → 2026

Stacked bars by venue, one bar per year. NeurIPS dominates the early record (2010–2017, ICLR/ICML absent from the corpus); ICLR takes over from 2020 onwards. The 2026 bar reaches 1,235 papers even without NeurIPS 2026, which had not yet run at snapshot time. The AlphaFold2 reference line marks the 2021 inflection point.

SVG (vector, editable): figures/corpus_overview.svg

Topic trajectories — 7 tags, 2017 → 2026

Per-year paper counts for the seven densest tags. protein climbs from ~2 papers/year (2018) to 294 in 2026 — a textbook post-AlphaFold2 explosion. single_cell lifts late (25 → 125 across 2020–2026). genomics roughly doubles between 2024 → 2025 (59 → 148). medical_imaging and clinical grow more steadily.

SVG (vector, editable): figures/topic_trajectories.svg

Both figures are regenerated directly from data/papers_master.json; the plotting code lives in the companion biotender-ai-bio-corpus repo.


🗂 Bro

Description

3,722 AI × Biology papers from ICLR / ICML / NeurIPS (2010–2026) — browsable by venue & year

Related repositories

Similar projects matched by category, topics, and programming language.

lopopolo
Featured
lopopolo GitHub avatar

harness-engineering

Harness Engineering is a methodology for improving coding agent outputs by carefully crafting the environment around them—providing curated context, tools, and executable constraints that encode an organization’s nonfunctional requirements and cumulative lessons.

AI & Machine LearningAI Agents
2,390
slvDev
Featured
slvDev GitHub avatar

esp32-ai

A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.

AI & Machine LearningLarge Language Models
1,960
littledivy
Featured
littledivy GitHub avatar

mimic

mimic captures traffic from any iOS or web app and automatically generates a Python client library that lets you call the app's API like a regular library.

AI & Machine Learning
1,482