RRepoGEO

REPOGEO REPORT · LITE

yangkky/Machine-learning-for-proteins

Default branch master · commit 4afcaab0 · scanned 5/19/2026, 5:48:04 AM

GitHub: 1,705 stars · 217 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
15 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface yangkky/Machine-learning-for-proteins, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Update README's main heading to clarify repo's purpose

    Why:

    CURRENT
    ## Papers on machine learning for proteins
    COPY-PASTE FIX
    ## A Curated, Collaborative List of Research Papers on Machine Learning for Proteins
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    machine-learning, proteins, bioinformatics, protein-engineering, computational-biology, research-papers, literature-review, protein-design, generative-models, representation-learning, protein-structure-prediction
  • mediumabout#3
    Enhance the repository description for clarity

    Why:

    CURRENT
    Listing of papers about machine learning for proteins.
    COPY-PASTE FIX
    A curated and collaboratively maintained list of research papers on machine learning applications in protein science, covering various models and applications like structure prediction, generative models, and directed evolution.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface yangkky/Machine-learning-for-proteins
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv · recommended 2×
  2. NeurIPS · recommended 2×
  3. ICLR · recommended 2×
  4. RECOMB · recommended 2×
  5. PubMed · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of machine learning research papers for protein applications?
    you: not recommended
    AI recommended (in order):
    1. PubMed
    2. Google Scholar
    3. arXiv
    4. BioRxiv
    5. MedRxiv
    6. NeurIPS
    7. ICML
    8. ICLR
    9. ISMB
    10. RECOMB
    11. GitHub

    AI recommended 11 alternatives but never named yangkky/Machine-learning-for-proteins. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to stay updated on the latest machine learning advancements in protein engineering and design?
    you: not recommended
    AI recommended (in order):
    1. Twitter/X
    2. Google DeepMind
    3. AlphaFold
    4. Meta AI
    5. Microsoft Research
    6. bioRxiv
    7. arXiv
    8. RoseTTAFold
    9. ESM
    10. ISMB/ECCB
    11. NeurIPS
    12. ICLR
    13. RECOMB
    14. FASEB Science Research Conferences
    15. Nature
    16. Nature Biotechnology
    17. Nature Methods
    18. Nature Machine Intelligence
    19. Science
    20. Science Translational Medicine
    21. Science Advances
    22. Cell
    23. Cell Systems
    24. Cell Chemical Biology
    25. Journal of Molecular Biology (JMB)
    26. Proteins: Structure, Function, and Bioinformatics
    27. PLoS Computational Biology
    28. LinkedIn Groups
    29. Reddit
    30. r/bioinformatics
    31. r/MachineLearning
    32. Generate Biomedicines
    33. DeepCure
    34. Baker Lab
    35. CASP

    AI recommended 35 alternatives but never named yangkky/Machine-learning-for-proteins. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of yangkky/Machine-learning-for-proteins?
    pass
    AI did not name yangkky/Machine-learning-for-proteins — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts yangkky/Machine-learning-for-proteins in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name yangkky/Machine-learning-for-proteins — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo yangkky/Machine-learning-for-proteins solve, and who is the primary audience?
    pass
    AI did not name yangkky/Machine-learning-for-proteins — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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yangkky/Machine-learning-for-proteins — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite