Does Biomni require a separate database download?

Yes, Biomni leverages an integrated data lake of approximately 11GB, covering gene databases, protein structures, and clinical datasets, which is typically downloaded upon first use.

What is the Biomni skill for Claude Code?

Biomni is an autonomous biomedical AI framework that allows Claude to execute complex research tasks like CRISPR design, drug discovery analysis, and genomic interpretation by combining LLM reasoning with code execution and biomedical databases.

Which biological domains does Biomni support?

Biomni is cross-domain, supporting genomics, proteomics, drug discovery (ADMET), clinical genomics, transcriptomics (RNA-seq), and laboratory protocol optimization.

Is the code executed by Biomni safe?

Biomni generates and executes code with system privileges to perform analysis. It is recommended to run the agent in isolated environments like Docker or VMs when processing sensitive data or complex scripts.

Biomni Biomedical AI

Name: Biomni Biomedical AI
Author: Activer007

byActiver007

Ciencia de Datos y ML

Automates complex biomedical research tasks including genomics, drug discovery, and clinical data analysis using an autonomous agent framework.

Acerca de

Biomni is a sophisticated biomedical AI agent framework developed by Stanford's SNAP lab, designed to streamline multi-step research workflows within Claude. It integrates large language models with autonomous code execution and a massive 11GB biomedical database, allowing researchers to automate tasks such as CRISPR screen design, single-cell RNA-seq analysis, and ADMET prediction. By bridging the gap between biological reasoning and computational execution, Biomni serves as a powerful assistant for scientists working in drug discovery, genomics, and molecular biology who need to move from natural language queries to data-driven insights.

Características Principales

Automated single-cell RNA-seq and GWAS analysis pipelines
0 GitHub stars
Dynamic code generation and execution for data processing
Integrated 11GB biomedical knowledge base and literature index
Autonomous multi-step biological reasoning and planning
Comprehensive drug discovery and ADMET prediction capabilities

Casos de Uso

Designing CRISPR knockout screens and prioritizing target genes based on pathway relevance
Analyzing single-cell RNA sequencing data for cell type annotation and differential expression
Predicting ADMET properties and identifying targets for drug discovery and compound optimization

Acerca de

Características Principales

Automated single-cell RNA-seq and GWAS analysis pipelines
0 GitHub stars
Dynamic code generation and execution for data processing
Integrated 11GB biomedical knowledge base and literature index
Autonomous multi-step biological reasoning and planning
Comprehensive drug discovery and ADMET prediction capabilities

Casos de Uso

Designing CRISPR knockout screens and prioritizing target genes based on pathway relevance
Analyzing single-cell RNA sequencing data for cell type annotation and differential expression
Predicting ADMET properties and identifying targets for drug discovery and compound optimization