Can I use Biomni for clinical genomics?

Yes, Biomni is specifically designed for clinical tasks such as rare disease diagnosis, variant pathogenicity interpretation, and phenotype-genotype mapping.

What specific biomedical databases does Biomni use?

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

How much storage does Biomni require?

Upon first use, Biomni downloads roughly 11GB of biomedical knowledge sources to your specified local data path.

How does Biomni handle code execution?

The agent autonomously generates and executes Python code to process datasets. For safety, it is recommended to run Biomni in isolated environments like Docker or VMs.

Is Biomni compatible with multiple LLM providers?

While optimized for Anthropic Claude (Sonnet and Opus), it supports OpenAI, Azure, Google Gemini, Groq, and AWS Bedrock via its configuration settings.

Biomni Biomedical AI Agent

Name: Biomni Biomedical AI Agent
Author: Microck

byMicrock

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Ciencia de Datos y ML

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

Acerca de

Biomni is an advanced autonomous agent framework designed to bridge the gap between large language models and complex biomedical research. By integrating reasoning with code execution and access to over 11GB of specialized biomedical databases, it enables researchers to perform sophisticated tasks such as CRISPR screening design, single-cell RNA-seq analysis, and ADMET prediction. This skill is particularly useful for bioinformaticians and research scientists who need a unified, AI-driven interface for multi-step biological reasoning and automated data analysis pipelines.

Características Principales

Integrated access to 11GB of curated biomedical databases and literature
Standardized evaluation framework for benchmarking biomedical task performance
Autonomous task decomposition and multi-step biological reasoning
Dynamic code generation and execution for custom data analysis pipelines
81 GitHub stars
Cross-domain capabilities spanning genomics, proteomics, and drug discovery

Casos de Uso

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

Acerca de

Características Principales

Integrated access to 11GB of curated biomedical databases and literature
Standardized evaluation framework for benchmarking biomedical task performance
Autonomous task decomposition and multi-step biological reasoning
Dynamic code generation and execution for custom data analysis pipelines
81 GitHub stars
Cross-domain capabilities spanning genomics, proteomics, and drug discovery

Casos de Uso

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