RRepoGEO

REPOGEO REPORT · LITE

OmicsML/awesome-deep-learning-single-cell-papers

Default branch main · commit c61f634a · scanned 6/12/2026, 6:18:00 PM

GitHub: 858 stars · 114 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 OmicsML/awesome-deep-learning-single-cell-papers, 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
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    A curated list of the latest research papers on deep learning methods applied to single-cell analysis, categorized by task.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    deep-learning, single-cell, omics, bioinformatics, computational-biology, awesome-list, research-papers, machine-learning, genomics, transcriptomics
  • mediumreadme#3
    Clarify the README's opening to emphasize 'curated list of papers'

    Why:

    CURRENT
    This repository keeps track of the latest papers on single-cell analysis with deep learning methods. We categorize them based on individual tasks.
    COPY-PASTE FIX
    This awesome list curates and categorizes the latest research papers on deep learning methods for single-cell analysis, organized by individual tasks.

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 OmicsML/awesome-deep-learning-single-cell-papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome Single Cell
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome Single Cell · recommended 1×
  2. Scverse ecosystem · recommended 1×
  3. BioRxiv · recommended 1×
  4. MedRxiv · recommended 1×
  5. AGBT · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of recent research in deep learning for single-cell analysis?
    you: not recommended
    AI recommended (in order):
    1. Awesome Single Cell
    2. Scverse ecosystem
    3. BioRxiv
    4. MedRxiv
    5. AGBT
    6. ISMB/ECCB
    7. Cell Symposia
    8. Nature Methods
    9. Nature Biotechnology
    10. Cell
    11. Science
    12. Genome Biology

    AI recommended 12 alternatives but never named OmicsML/awesome-deep-learning-single-cell-papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest deep learning methods applied to single-cell omics data analysis tasks?
    you: not recommended
    AI recommended (in order):
    1. scVI
    2. scDHA
    3. DeepWalk
    4. scDeepCluster
    5. DESC
    6. scAnnotate
    7. CellAssign
    8. scANVI
    9. DeepSTREAM
    10. scVAEIT
    11. TotalVI
    12. DeepGRN
    13. SCENIC+

    AI recommended 13 alternatives but never named OmicsML/awesome-deep-learning-single-cell-papers. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 OmicsML/awesome-deep-learning-single-cell-papers?
    pass
    AI named OmicsML/awesome-deep-learning-single-cell-papers explicitly

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

  • If a team adopts OmicsML/awesome-deep-learning-single-cell-papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OmicsML/awesome-deep-learning-single-cell-papers 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 OmicsML/awesome-deep-learning-single-cell-papers solve, and who is the primary audience?
    pass
    AI did not name OmicsML/awesome-deep-learning-single-cell-papers — 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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