Add GenoTEX and GenoMAS papers on LLM agents for gene expression analysis

[Liu-Hy]

Thank you for maintaining this excellent resource for the community. In this PR, we request to add two recent papers from our research lab on LLM-based agents for automated gene expression analysis:

Papers Added:

1. GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis

• Publication: MLCB 2025 (Oral)
• arXiv: https://arxiv.org/abs/2406.15341
• Website: https://liu-hy.github.io/GenoTEX/
• Code: https://github.com/Liu-Hy/GenoTEX
• Key contribution: A comprehensive benchmark dataset with 1,384 gene-trait association analysis problems, 911 datasets, and 238K lines of expert-curated analysis code for evaluating automated methods for gene expression analysis.

2. GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis

• arXiv: https://arxiv.org/abs/2507.21035
• Website: https://liu-hy.github.io/GenoMAS/
• Code: https://github.com/Liu-Hy/GenoMAS
• Key contribution: A multi-agent system with six specialized LLM agents and a guided-planning framework that achieves state-of-the-art performance on gene expression analysis tasks, with significant improvements over prior methods (CSC: 89.13%, F₁: 60.48%).

Both papers focus on leveraging LLM-based agents to automate the labor-intensive process of gene expression data analysis, which is highly relevant to the AI+Biopharmaceutical section of this repository.

We have:

• Followed the existing format and style of the repository
• Placed the entries in the AI+Biopharmaceutical section (entries #57 and #58)
• Included all relevant information: Chinese interpretation links, research teams, related research details, publication venues, and paper links
• Updated the table of contents accordingly

Thank you for considering our request!