Is Biomni suitable for clinical genomics?

Yes, it can be used for rare disease diagnosis, variant pathogenicity interpretation, and phenotype-genotype mapping by leveraging integrated clinical datasets.

Which LLMs are compatible with the Biomni skill?

While Claude (Sonnet 4 or Opus) is recommended for best results, Biomni also supports OpenAI GPT-4, Google Gemini, Groq, and AWS Bedrock models.

What kind of data does Biomni have access to?

Biomni integrates approximately 11GB of data, including Ensembl, UniProt, ClinVar, PubMed abstracts, and various pathway databases like KEGG and Reactome.

Can Biomni execute code autonomously?

Yes, Biomni generates and executes Python code to process data and create analysis pipelines. It is recommended to run these tasks in isolated environments like Docker for security.

Biomni Biomedical AI Agent

Name: Biomni Biomedical AI Agent
Author: Activer007

byActiver007

数据科学与机器学习

Automates complex biomedical research tasks including genomics analysis, drug discovery, and CRISPR screening using integrated data and code execution.

关于

Biomni is a specialized autonomous agent framework developed by Stanford's SNAP lab, designed to streamline multi-step biomedical research. By combining LLM reasoning with an integrated 11GB knowledge base and dynamic code execution, it empowers researchers to perform sophisticated tasks across genomics, proteomics, and clinical analysis. Whether you are designing CRISPR screens, interpreting GWAS results, or predicting drug ADMET properties, Biomni handles task decomposition, data retrieval, and analysis pipeline creation to deliver actionable biological insights directly within your development environment.

主要功能

Dynamic Python code generation and execution for data processing
Multi-step autonomous biological reasoning and task planning
Standardized evaluation framework for benchmarking biomedical task accuracy
Integrated access to 11GB of curated biomedical databases and literature
Cross-domain capabilities spanning genomics, proteomics, and drug discovery
0 GitHub stars

使用场景

Analyzing single-cell RNA-seq datasets for cell type annotation and differential expression
Predicting ADMET properties and identifying targets for drug discovery projects
Designing genome-wide CRISPR knockout screens and prioritizing target genes

关于

主要功能

Dynamic Python code generation and execution for data processing
Multi-step autonomous biological reasoning and task planning
Standardized evaluation framework for benchmarking biomedical task accuracy
Integrated access to 11GB of curated biomedical databases and literature
Cross-domain capabilities spanning genomics, proteomics, and drug discovery
0 GitHub stars

使用场景

Analyzing single-cell RNA-seq datasets for cell type annotation and differential expression
Predicting ADMET properties and identifying targets for drug discovery projects
Designing genome-wide CRISPR knockout screens and prioritizing target genes