yangkky/Machine-learning-for-proteins

• highabout#1

  Update the 'About' description to emphasize its nature as a curated list

  原因：

  当前

  Listing of papers about machine learning for proteins.

  复制粘贴的修复

  复制

  A public, collaborative, and curated list of papers on machine learning applications in protein science, categorized by application and model type.

  待办完成忽略
• highreadme#2

  Reposition the README's opening to clearly state it's a curated list

  原因：

  当前

  ## Papers on machine learning for proteins
  
  ### Background
  
  We recently released a review of machine learning methods in protein engineering, but the field changes so fast and there are so many new papers that any static document will inevitably be missing important work. This format also allows us to broaden the scope beyond engineering-specific applications. We hope that this will be a useful resource for people interested in the field.
  
  To the best of our knowledge, this is the first public, collaborative list of machine learning papers on protein applications.

  复制粘贴的修复

  复制

  ## A Curated, Collaborative List of Papers on Machine Learning for Proteins
  
  ### Background
  
  This repository serves as a public, collaborative, and continuously updated list of machine learning papers applied to protein science. While we recently released a review of machine learning methods in protein engineering, the field changes so fast that any static document inevitably misses important work. This format allows us to broaden the scope beyond engineering-specific applications and provide a useful resource for people interested in the field.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Where can I find a comprehensive list of machine learning papers for protein applications?

  你：未被推荐
  AI 推荐顺序：

  1. PubMed
  2. Google Scholar
  3. arXiv
  4. Bioinformatics
  5. Nature Methods
  6. Nature Biotechnology
  7. Nature Machine Intelligence
  8. Journal of Chemical Information and Modeling (JCIM)
  9. PLoS Computational Biology
  10. GitHub

  AI 推荐了 10 个替代方案，却始终没点名 yangkky/Machine-learning-for-proteins。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are the latest machine learning techniques for protein engineering and variant prediction?

  你：未被推荐
  AI 推荐顺序：

  1. DeepSequence
  2. ProteinVAE
  3. AlphaFold
  4. RFdiffusion
  5. ESM-2
  6. ProtT5
  7. MSA Transformer
  8. DeepMind's AlphaFold-driven RL for protein design

  AI 推荐了 8 个替代方案，却始终没点名 yangkky/Machine-learning-for-proteins。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of yangkky/Machine-learning-for-proteins?

  pass
  ✓AI 明确点名了 yangkky/Machine-learning-for-proteins

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts yangkky/Machine-learning-for-proteins in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 yangkky/Machine-learning-for-proteins

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo yangkky/Machine-learning-for-proteins solve, and who is the primary audience?

  pass
  ⚠AI 未点名 yangkky/Machine-learning-for-proteins —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？