Is the code execution in Biomni secure?

Biomni executes generated code with system privileges. It is recommended to run the skill in isolated environments like Docker or virtual machines for production use.

How does Biomni handle biomedical data?

It integrates a massive 11GB data lake containing gene databases, protein structures, and clinical datasets, allowing the agent to perform informed analysis without manual data gathering.

Which LLMs are compatible with Biomni?

While Claude 3.5 Sonnet is highly recommended, Biomni also supports OpenAI GPT-4, Google Gemini, Groq, and AWS Bedrock models.

What is the Biomni Claude Code Skill?

Biomni is an autonomous AI agent framework from Stanford's SNAP lab specifically built to handle complex, multi-step biomedical research tasks using Claude and other LLMs.

Can Biomni assist with drug discovery?

Yes, it is optimized for drug discovery tasks including ADMET prediction, target identification, and compound optimization using integrated molecular biology knowledge.

Biomni Biomedical AI Agent

Name: Biomni Biomedical AI Agent
Author: Activer007

byActiver007

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Ciencia de Datos y ML

Executes complex biomedical research tasks including genomics analysis, drug discovery, and molecular biology workflows through an autonomous agent framework.

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Biomni is an open-source autonomous agent framework developed by Stanford's SNAP lab, designed specifically for high-level biomedical research. It bridges the gap between LLM reasoning and practical execution by automating multi-step tasks such as CRISPR screen design, single-cell RNA-seq analysis, and ADMET prediction. By integrating approximately 11GB of curated biomedical databases and literature with dynamic code execution, Biomni enables researchers to perform data-driven discovery across genomics, proteomics, and clinical contexts within a unified interface.

Características Principales

1 GitHub stars
Autonomous multi-step biological reasoning and task planning
Automated code generation and execution for data processing pipelines
Integrated access to 11GB of curated biomedical databases and literature
Cross-domain support for genomics, drug discovery, and clinical analysis
Built-in evaluation framework for benchmarking biomedical task performance

Casos de Uso

Analyzing single-cell RNA-seq datasets for cell type annotation and differential expression
Designing and prioritizing CRISPR screening experiments
Predicting ADMET properties and identifying targets for drug discovery

Acerca de

Características Principales

1 GitHub stars
Autonomous multi-step biological reasoning and task planning
Automated code generation and execution for data processing pipelines
Integrated access to 11GB of curated biomedical databases and literature
Cross-domain support for genomics, drug discovery, and clinical analysis
Built-in evaluation framework for benchmarking biomedical task performance

Casos de Uso

Analyzing single-cell RNA-seq datasets for cell type annotation and differential expression
Designing and prioritizing CRISPR screening experiments
Predicting ADMET properties and identifying targets for drug discovery