What specific research tasks can I perform?

You can use it for CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, and clinical genomics mapping.

How does Biomni handle biomedical data?

It integrates approximately 11GB of curated databases covering genes, proteins, clinical datasets, and literature, which it uses for retrieval-augmented reasoning and analysis.

Biomni is an open-source autonomous AI agent framework from Stanford's SNAP lab designed for complex biomedical research tasks including genomics, drug discovery, and molecular biology.

Which AI models work best with Biomni?

While it supports various providers, Anthropic's Claude 3.5 Sonnet and Claude 3 Opus are highly recommended for the best balance of speed and reasoning quality.

Can Biomni execute code?

Yes, Biomni autonomously generates and executes Python code to process data, create analysis pipelines, and interpret results in real-time.

Biomni Biomedical AI

Name: Biomni Biomedical AI
Author: BbgnsurfTech

byBbgnsurfTech

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数据科学与机器学习

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

关于

Biomni is a sophisticated autonomous agent framework developed by Stanford's SNAP lab, designed to streamline complex biomedical research. It leverages Claude's reasoning capabilities alongside integrated access to 11GB of curated biomedical databases to perform tasks like CRISPR screening design, single-cell RNA-seq analysis, and ADMET prediction. This skill allows researchers to automate data analysis pipelines, interpret GWAS results, and optimize laboratory protocols through a unified AI-driven interface that combines code execution with deep domain knowledge.

主要功能

Comprehensive support for CRISPR, scRNA-seq, and GWAS workflows
Integrated access to 11GB of genomics, proteomics, and clinical databases
3 GitHub stars
Autonomous multi-step biological reasoning and task planning
Dynamic analysis pipeline creation and code execution
Advanced ADMET property prediction for drug discovery

使用场景

Predicting pharmacokinetics and toxicity profiles for novel drug candidates
Automating quality control and cell type annotation for single-cell RNA-seq datasets
Designing and prioritizing genes for genome-wide CRISPR knockout screens

关于

主要功能

Comprehensive support for CRISPR, scRNA-seq, and GWAS workflows
Integrated access to 11GB of genomics, proteomics, and clinical databases
3 GitHub stars
Autonomous multi-step biological reasoning and task planning
Dynamic analysis pipeline creation and code execution
Advanced ADMET property prediction for drug discovery

使用场景

Predicting pharmacokinetics and toxicity profiles for novel drug candidates
Automating quality control and cell type annotation for single-cell RNA-seq datasets
Designing and prioritizing genes for genome-wide CRISPR knockout screens