Does Biomni come with its own data?

Yes, Biomni integrates approximately 11GB of biomedical data, including Ensembl, UniProt, and PubMed indices, which are automatically downloaded upon first use.

Which research tasks can Biomni automate?

It excels at CRISPR screening design, single-cell RNA-seq analysis, GWAS interpretation, ADMET prediction for drugs, and clinical genotype-phenotype mapping.

What is the Biomni Claude Skill?

Biomni is an autonomous AI framework developed by Stanford's SNAP lab that allows Claude to execute complex research tasks in fields like genomics and drug discovery by combining reasoning with code execution.

Is it safe to run Biomni locally?

Since Biomni executes LLM-generated code with system privileges, it is recommended to run it within isolated environments like Docker or virtual machines for production use.

Which LLM models are recommended for Biomni?

While it supports multiple providers, Anthropic Claude 3.5 Sonnet is highly recommended for its superior balance of biomedical reasoning and execution speed.

Biomni Biomedical AI Agent

Name: Biomni Biomedical AI Agent
Author: Microck

byMicrock

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데이터 과학 및 ML

Executes autonomous multi-step biomedical research tasks spanning genomics, drug discovery, and clinical analysis.

소개

Biomni is a sophisticated biomedical AI agent framework from Stanford's SNAP lab that enables Claude to perform autonomous research tasks. By integrating LLM reasoning with code execution and access to an 11GB curated biomedical database, it handles complex workflows like CRISPR screening design, single-cell RNA-seq analysis, and ADMET prediction. This skill is ideal for researchers needing automated data processing pipelines and knowledge retrieval across proteomics, genomics, and clinical datasets.

주요 기능

89 GitHub stars
Built-in evaluation framework to benchmark biomedical task performance
Autonomous multi-step biological reasoning and task decomposition
Integrated 11GB database of genomics, proteins, and clinical literature
Automated code generation and execution for bioinformatics pipelines
Cross-domain problem solving for drug discovery and molecular biology

사용 사례

Automating single-cell RNA-seq workflows including clustering and annotation
Designing and prioritizing genes for genome-wide CRISPR knockout screens
Predicting ADMET properties and identifying targets for drug discovery candidates

소개

주요 기능

89 GitHub stars
Built-in evaluation framework to benchmark biomedical task performance
Autonomous multi-step biological reasoning and task decomposition
Integrated 11GB database of genomics, proteins, and clinical literature
Automated code generation and execution for bioinformatics pipelines
Cross-domain problem solving for drug discovery and molecular biology

사용 사례

Automating single-cell RNA-seq workflows including clustering and annotation
Designing and prioritizing genes for genome-wide CRISPR knockout screens
Predicting ADMET properties and identifying targets for drug discovery candidates