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README.md

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+---
+license: mit
+datasets:
+- tahoebio/Tahoe-100M
+tags:
+- tahoe-deepdive
+- hackathon
+- tahoe-100M
+---
+
+# Team Name
+**Kepler**
+## Members
+- Ashton Teng @ashtonteng 
+- Quinn Leng
+- [Affiliation, GitHub handles if applicable]
+
+# Project
+## Title
+Kepler: Natural Language AI Agent for Tahoe-100M Exploration
+
+## Overview
+Kepler lets biologists query the Tahoe-100M dataset in plain English, automating data access, analysis, and visualization without coding.
+
+## Motivation
+High-dimensional datasets like Tahoe-100M require heavy compute setup, tool expertise, and programming skill—barriers that slow scientific insight.
+
+We demonstrate the capability for the agent to allow for users to perform simple analyses with natural language.
+
+## Methods
+- Extracted a pseudobulked subset with Vision differential expression scores.  
+- Loaded metadata tables for cell lines, drugs, and gene sets.  
+- Built an AI agent to translate natural-language queries into analysis code and visual outputs.
+
+## Results
+Demo query: “Which pathways are upregulated in BRAF.V600E mutant models after inhibitor treatment?”  
+Agent automatically filtered the data, ran the analysis, and generated plots with interpretations.
+
+## Discussion
+- **Scalability:** Move initial subsetting to DuckDB or Databricks for larger subsets.  
+- **Knowledge alignment:** Enhance the agent’s scientific context for broader, valid analyses.  
+- **Next steps:** Expand to full Tahoe-100M and optimize compute pipeline.

Tahoe-100M.pdf

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