RRepoGEO

REPOGEO REPORT · LITE

sacdallago/bio_embeddings

Default branch develop · commit efb9801f · scanned 6/7/2026, 9:36:41 AM

GitHub: 508 stars · 70 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 sacdallago/bio_embeddings, 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 H1 and introductory paragraph to clarify pipeline role

    Why:

    CURRENT
    # Bio Embeddings Resources to learn about bio_embeddings:
    COPY-PASTE FIX
    # Bio Embeddings: A Unified Pipeline for Protein Sequence Embeddings
This project provides a comprehensive, consistent interface and reproducible workflows for generating and applying diverse language model-based protein sequence representations (e.g., SeqVec, ProtTrans, UniRep) for transfer-learning, structure prediction, and function analysis.
  • mediumtopics#2
    Add topics to clarify framework role and integrated models

    Why:

    CURRENT
    bio-embeddings, embedders, language-model, machine-learning, pipeline, protein-prediction, protein-sequences, protein-structure, sequence-embeddings
    COPY-PASTE FIX
    bio-embeddings, embedders, language-model, machine-learning, pipeline, protein-prediction, protein-sequences, protein-structure, sequence-embeddings, protein-language-models, bioinformatics-framework, deep-learning-toolkit, prottrans, seqvec, esm
  • lowreadme#3
    Add a concise differentiator statement to the README

    Why:

    COPY-PASTE FIX
    Unlike individual model implementations, bio_embeddings offers a unified, zero-friction interface to a diverse range of pre-trained protein language models, ensuring reproducible workflows and handling complexities like CUDA OOM abstraction.

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 sacdallago/bio_embeddings
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
rostlab/ProtTrans
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. rostlab/ProtTrans · recommended 2×
  2. facebookresearch/esm · recommended 1×
  3. AlQuraishiLab/Ankh · recommended 1×
  4. rostlab/SeqVec · recommended 1×
  5. deepmsa/DeepMSA · recommended 1×
  • CATEGORY QUERY
    How can I generate embeddings for protein sequences using machine learning models?
    you: not recommended
    AI recommended (in order):
    1. ESM-2 (facebookresearch/esm)
    2. ProtT5-XL-U50 (rostlab/ProtTrans)
    3. ProtBERT-BFD (rostlab/ProtTrans)
    4. Ankh (AlQuraishiLab/Ankh)
    5. SeqVec (rostlab/SeqVec)
    6. DeepMSA (deepmsa/DeepMSA)
    7. AlphaFold2 (deepmind/alphafold)

    AI recommended 7 alternatives but never named sacdallago/bio_embeddings. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help predict protein structure and function from amino acid sequences?
    you: not recommended
    AI recommended (in order):
    1. AlphaFold2
    2. RoseTTAFold
    3. I-TASSER
    4. SWISS-MODEL
    5. Phyre2
    6. HHpred
    7. InterPro

    AI recommended 7 alternatives but never named sacdallago/bio_embeddings. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 sacdallago/bio_embeddings?
    pass
    AI did not name sacdallago/bio_embeddings — 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 sacdallago/bio_embeddings in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sacdallago/bio_embeddings 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 sacdallago/bio_embeddings solve, and who is the primary audience?
    pass
    AI named sacdallago/bio_embeddings explicitly

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

Embed your GEO score

Drop this badge into the README of sacdallago/bio_embeddings. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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sacdallago/bio_embeddings — 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