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Bioconductor Gosemsim

Organization
bioMate-AI
bioconductor-gosemsim

The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R packag

Overview

PublisherbioMate-AI
Repositorybiomate-bioconductor-kb
Skill namebioconductor-gosemsim
Stars
804
Forks
66
Bundled files
Instructions only
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bioMate-AI on GitHub. Read the source before you install it.

Installation

Install the Bioconductor Gosemsim AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/genomics/gosemsim
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bioMate-AI/biomate-bioconductor-kb.git /tmp/biomate-bioconductor-kb
mkdir -p .claude/skills
cp -r /tmp/biomate-bioconductor-kb/skills/genomics/gosemsim .claude/skills/bioconductor-gosemsim
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bioconductor Gosemsim in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bioconductor Gosemsim on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bioconductor Gosemsim is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GOSemSim

GOSemSim is an R package designed for semantic similarity analysis among Gene Ontology (GO) terms and gene products.

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

  • Version: 2.38.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Imports: AnnotationDbi, DBI, digest, GO.db, rlang, yulab.utils
  • Install: BiocManager::install("GOSemSim")

When to Use

  • Measuring semantic similarity among GO terms.
  • Computing functional similarity among gene products.
  • Performing GO semantic similarity analyses within stem cell transcriptional networks or other biomedical contexts.

When NOT to Use

  • For general biomedical knowledge mining tasks that do not involve Gene Ontology semantic similarity, refer to broader biomedical knowledge mining resources.
  • When performing analyses that do not require quantitative semantic comparisons of GO annotations.

Data Requirements

  • Gene Ontology (GO) annotations.
  • Gene and gene product datasets requiring functional similarity comparison.

Key Parameters

  • No specific R function parameters are detailed in the provided short vignette text. Please refer to the external full documentation for parameter specifications.

Best Practices

  • Refer to the comprehensive online book (https://yulab-smu.top/biomedical-knowledge-mining-book/) for the full vignette, detailed workflows, and tutorials.
  • When seeking assistance, post questions to the Bioconductor support site and tag the post with GOSemSim.
  • Cite the appropriate primary publications when using GOSemSim in published research.

Common Pitfalls

  • Searching for detailed code examples in the package's built-in short vignette; fix this by visiting the external online biomedical knowledge mining book for the complete documentation.
  • Failing to tag support questions properly; fix this by tagging posts with GOSemSim on the Bioconductor support site.

Alternatives

  • Other Bioconductor packages designed for functional annotation and enrichment analysis (though GOSemSim is specifically focused on semantic similarity).

Citations

  • Yu G. Gene Ontology Semantic Similarity Analysis Using GOSemSim. In: Kidder B. (eds) Stem Cell Transcriptional Networks. Methods in Molecular Biology, 2020, 2117:207-215. Humana, New York, NY.
  • Yu G, Li F, Qin Y, Bo X, Wu Y and Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics, 2010, 26(7):976-978.

References

Run this on BioMate

This skill is the knowledge layer — when, why, and how to use gosemsim. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open gosemsim on BioMate →

Frequently asked questions

What does the Bioconductor Gosemsim AI skill do?

The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R packag

Why use Bioconductor Gosemsim on TypingMind?

Because you install it once and use it with any model. Bioconductor Gosemsim is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bioconductor Gosemsim in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bioMate-AI/biomate-bioconductor-kb/tree/main/skills/genomics/gosemsim. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bioconductor Gosemsim?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bioconductor Gosemsim?

As many as you like. As long as a model supports skills, you can use Bioconductor Gosemsim with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bioconductor Gosemsim AI skill free?

It is published on GitHub by bioMate-AI. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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