RRepoGEO

REPOGEO REPORT · LITE

jerryji1993/DNABERT

Default branch master · commit b6da04ec · scanned 6/3/2026, 1:58:22 PM

GitHub: 752 stars · 178 forks

AI VISIBILITY SCORE
89 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 jerryji1993/DNABERT, 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
  • mediumreadme#1
    Clarify DNABERT's unique value proposition in the README introduction

    Why:

    CURRENT
    This repository includes the implementation of 'DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome'.
    COPY-PASTE FIX
    This repository includes the implementation of 'DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome'. Unlike general-purpose language models, DNABERT is specifically pre-trained on a massive corpus of genomic data, treating DNA k-mers as tokens to learn rich, contextual representations of DNA sequences.
  • mediumreadme#2
    Emphasize HuggingFace ecosystem compatibility in the README

    Why:

    CURRENT
    We extended codes from huggingface and adapted them to the DNA scenario.
    COPY-PASTE FIX
    Built upon the HuggingFace Transformers library, DNABERT offers seamless integration for researchers, with pre-trained models readily available on HuggingFace and adapted for DNA scenarios.
  • lowtopics#3
    Expand topics with more specific bioinformatics terms

    Why:

    CURRENT
    deep-learning, dnabert-model, genome, gpu, kmer, kmer-format, machine-learning, natural-language-processing, nlp, sequence
    COPY-PASTE FIX
    deep-learning, dnabert-model, genome, gpu, kmer, kmer-format, machine-learning, natural-language-processing, nlp, sequence, genomic-sequence-analysis, dna-language-model, sequence-prediction, bioinformatics-models

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
2 / 2
100% of queries surface jerryji1993/DNABERT
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
15%
Of all named tools, what % are you?
Top rival
HyenaDNA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. HyenaDNA · recommended 2×
  2. Enformer · recommended 2×
  3. GenomicBERT · recommended 1×
  4. BigBird · recommended 1×
  5. Longformer · recommended 1×
  • CATEGORY QUERY
    How to apply transformer models for genomic sequence analysis and understanding DNA language?
    you: #1
    AI recommended (in order):
    1. DNABERT ← you
    2. GenomicBERT
    3. HyenaDNA
    4. Enformer
    5. BigBird
    6. Longformer
    7. Transformers
    Show full AI answer
  • CATEGORY QUERY
    What are pre-trained deep learning models for DNA sequence classification and functional prediction?
    you: #3
    AI recommended (in order):
    1. Enformer
    2. HyenaDNA
    3. DNABERT ← you
    4. DeepSEA
    5. DanQ
    6. Geneformer
    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 jerryji1993/DNABERT?
    pass
    AI named jerryji1993/DNABERT explicitly

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

  • If a team adopts jerryji1993/DNABERT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jerryji1993/DNABERT 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 jerryji1993/DNABERT solve, and who is the primary audience?
    pass
    AI named jerryji1993/DNABERT explicitly

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

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