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README.md

@@ -1,23 +1,4 @@
----
-license: cc-by-4.0
-task_categories:
-- text-generation
-- text2text-generation
-- table-question-answering
-- tabular-regression
-language:
-- en
-tags:
-- ai4science
-- agent
-- llm
-- genomics
-- biology
-pretty_name: 'GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis'
-size_categories:
-- 100B<n<1T
----
-# GenoTEX: A Benchmark for Automated Gene Expression Data Analysis
+# GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis
 
 <div align="center">
   <img src="./imgs/genotex_logo.png" alt="GenoTEX Logo" width="200px"/>
@@ -53,6 +34,7 @@ The below figure illustrates our benchmark curation process. For detailed inform
 - [Usage](#usage)
 - [Citation](#citation)
 - [License](#license)
+- [Acknowledgments](#acknowledgments)
 
 <a id="dataset-overview"></a>
 ## 📊 Dataset Overview
@@ -75,11 +57,11 @@ Each problem in the benchmark involves identifying genes associated with a speci
 
 GenoTEX is distributed in two ways:
 1. **GitHub repository + Cloud storage**: In this approach, we host the code and documentation in the [GitHub repository](https://github.com/Liu-Hy/GenoTEX), while the data is accessible through separate cloud storage links below. This approach allows for efficient access to the analysis methods and their latest updates, while keeping the large data files separate.
-  - Data Available at: [[Google Drive](https://drive.google.com/drive/folders/1ZQ8AflAecW61SrNclaMby-6x9GLCpJoW) | [Baidu Cloud Disk](https://pan.baidu.com/s/1mKfBRiBNY0GUK6LRLnn7UA?pwd=1234)]. 
+  - Data Available at: [ [Google Drive](https://drive.google.com/drive/folders/1ZQ8AflAecW61SrNclaMby-6x9GLCpJoW) | [Baidu Cloud Disk](https://pan.baidu.com/s/1mKfBRiBNY0GUK6LRLnn7UA?pwd=1234) ] 
   - Total data size: 82.0 GB — Please ensure you have sufficient disk space before downloading.
 
 2. **Complete Datasets on Data Platforms**: We also provide a complete, bundled version (code + data) on [Kaggle](https://www.kaggle.com/datasets/haoyangliu14/genotex-llm-agent-benchmark-for-genomic-analysis) and [Hugging Face Hub](https://huggingface.co/datasets/Liu-Hy/GenoTEX).
-These versions are convenient for users who prefer a single download and want to leverage the rich functionalities of these platforms. 
+These versions are convenient for users who prefer a single download and want to leverage the functionalities of these platforms. 
 
 ### The Data Part
 
@@ -303,13 +285,13 @@ The code part of the benchmark includes:
 
 - **For the GitHub version**
 
-    (1). Clone this repository
+    (1) Clone this repository:
 
       ```bash
       git clone https://github.com/Liu-Hy/GenoTEX.git
       cd GenoTEX
       ```
-    (2). Download the data folders ('metadata', 'input', 'output') from the provided cloud storage links, and place them in the root directory of this repository.
+    (2) Download the data folders ('metadata', 'input', 'output') from the provided cloud storage links, and place them in the root directory of this repository.
 - **For the bundled version**
 
   Download the dataset folder containing code and data directly from the data platform.
@@ -387,4 +369,9 @@ If you use GenoTEX in your research, please cite our paper:
 <a id="license"></a>
 ## ⚖️ License
 
-The GenoTEX dataset is released under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which allows for broad usage while protecting the rights of the creators. The authors bear full responsibility for ensuring that the dataset adheres to this license and for any potential violations of rights. For the full license text, please see the [LICENSE.md](./LICENSE.md) file in this repository.
+The GenoTEX dataset is released under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, which allows for broad usage while protecting the rights of the creators. The authors bear full responsibility for ensuring that the dataset adheres to this license and for any potential violations of rights. For the full license text, please see the [LICENSE.md](./LICENSE.md) file in this repository.
+
+<a id="acknowledgments"></a>
+## 🙏 Acknowledgments
+
+This research was supported by the National AI Research Resource (NAIRR) under grant number 240283. We thank [Yijiang Li](https://williamium3000.github.io/) for his great suggestions to the baseline method, and [Jinglin Jian](https://scholar.google.com/citations?hl=en&user=pXY2xYkAAAAJ), [Jianrong Lu](https://scholar.google.com/citations?hl=en&user=k-oe9TUAAAAJ), [Shuyi Guo](https://github.com/Shuyi1011), Taincheng Xing, Yuxuan Cheng, Jinglei Zhu, Mianchen Zhang, Miantong Zhang for their valuable contribution to the benchmark curation.

code/regress.py

@@ -86,6 +86,9 @@ for i, (trait, condition) in enumerate(all_pairs):
 
                 trait_data[condition] = predicted_condition
 
+                # Remove gene regressors from trait_data to avoid multicollinearity
+                trait_data = trait_data.drop(columns=regressors, errors='ignore')
+
             Y = trait_data[trait].values
             Z = trait_data[condition].values
             X = trait_data.drop(columns=[trait, condition]).values