RRepoGEO

REPOGEO REPORT · LITE

LirongWu/awesome-protein-representation-learning

Default branch main · commit 97d848f9 · scanned 6/10/2026, 7:13:25 PM

GitHub: 684 stars · 75 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
22 /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
1 / 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 LirongWu/awesome-protein-representation-learning, 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
    Reposition README opening to explicitly state 'awesome list' and include repo name

    Why:

    CURRENT
    This repository contains a list of papers on the **Protein Representation Learning (PRL)**, we categorize them based on their published years. We will try to make this list updated. If you found any error or any missed paper, please don't hesitate to open issues or pull requests.
    COPY-PASTE FIX
    This **awesome-protein-representation-learning** repository is an **awesome list** of papers on **Protein Representation Learning (PRL)**, meticulously categorized by publication year. It serves as a comprehensive, up-to-date resource for researchers and practitioners. We strive to keep this list updated; please open issues or pull requests for any errors or missed papers.
  • highhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/LirongWu/awesome-protein-representation-learning
  • mediumtopics#3
    Add 'awesome-list' topic

    Why:

    CURRENT
    protein-embedding, protein-modeling, protein-representation-learning, protein-sequences, representation-learning
    COPY-PASTE FIX
    awesome-list, protein-embedding, protein-modeling, protein-representation-learning, protein-sequences, representation-learning

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 LirongWu/awesome-protein-representation-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. PubMed · recommended 1×
  4. PubMed Central (PMC) · recommended 1×
  5. NeurIPS · recommended 1×
  • CATEGORY QUERY
    Where can I find recent research on learning effective protein embeddings?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. PubMed
    4. PubMed Central (PMC)
    5. NeurIPS
    6. ICML
    7. ISMB
    8. RECOMB
    9. GitHub
    10. ESM-2
    11. ProtT5
    12. AlphaFold
    13. ESM
    14. Twitter
    15. DeepMind
    16. MetaAI
    17. SalesforceResearch

    AI recommended 17 alternatives but never named LirongWu/awesome-protein-representation-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the state-of-the-art techniques for generating protein sequence representations?
    you: not recommended
    AI recommended (in order):
    1. ESM (Evolutionary Scale Modeling)
    2. ProtTrans (Protein Transformers)
    3. Ankh
    4. ProGen
    5. UniRep (Universal Representation of Proteins)
    6. SeqVec

    AI recommended 6 alternatives but never named LirongWu/awesome-protein-representation-learning. 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 LirongWu/awesome-protein-representation-learning?
    pass
    AI did not name LirongWu/awesome-protein-representation-learning — 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 LirongWu/awesome-protein-representation-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LirongWu/awesome-protein-representation-learning explicitly

    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 LirongWu/awesome-protein-representation-learning solve, and who is the primary audience?
    pass
    AI did not name LirongWu/awesome-protein-representation-learning — 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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MARKDOWN (README)
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LirongWu/awesome-protein-representation-learning — 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