mragent 0.2.0

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MRAgent: An LLM-based Automated Agent for Causal Knowledge Discovery in Disease via Mendelian Randomization

Abstract

Understanding causality in medical research is essential for developing effective interventions and diagnostic tools. Mendelian Randomization (MR) is a pivotal method for inferring causality through genetic data. However, MR analysis often requires pre-identification of exposure-outcome pairs from clinical experience or literature, which can be challenging to obtain. This poses difficulties for clinicians investigating causal factors of specific diseases. To address this, we introduce MRAgent, an innovative automated agent leveraging Large Language Models (LLMs) to enhance causal knowledge discovery in disease research. MRAgent autonomously scans scientific literature, discovers potential exposure-outcome pairs, and performs MR causal inference using extensive Genome-Wide Association Study (GWAS) data. We conducted both automated and human evaluations to compare different LLMs in operating MRAgent and provided a proof-of-concept case to demonstrate the complete workflow. MRAgent's capability to conduct large-scale causal analyses represents a significant advancement, equipping researchers and clinicians with a robust tool for exploring and validating causal relationships in complex diseases.

MRAgent Architecture

MRAgent Workflow

Preparation

LLMs API Key

Before using MRAgent, you need to obtain the API Key for the LLMs, or run the LLM locally. The following LLM APIs are currently supported:

• OpenAI GPT
• Google Gemini
• Anthropic Claude
• Aliyun Qwen

You can also run LLM locally, and we currently support all open source models running on ollama:

• Ollama

You need to follow the steps to install ollama and follow the ollama python support package:

curl -fsSL https://ollama.com/install.sh | sh
pip install ollama

GWAS token

You need to get the GWAS token for the OpenGWAS data.

• OpenGWAS API

Usage

"Knowledge Discovery" mode

In the Knowledge Discovery mode, upon inputting a specific disease, the MRAgent autonomously scans and analyzes relevant literature from PubMed to identify potential exposures or outcomes associated with the disease. It then performs Mendelian randomization analysis to ascertain causal relationships between the disease and the identified exposures or outcomes, ultimately generating a comprehensive analysis report.

Run agent_workflow.py to start the MRAgent in the "Knowledge Discovery" mode:

Class

class MRAgent(self, mode='O', exposure=None, outcome=None, AI_key=None, model='MR', num=100, bidirectional=False,
                 synonyms=True, introduction=True, LLM_model='gpt-4o', gwas_token=None)

Parameters:

• mode: str, 'O' or 'E' optional (default='O')
  • The mode of the MRAgent. 'O' for "Knowledge Discovery" mode, the given disease is the outcome. 'E' for "Knowledge Discovery" mode, the given disease is the exposure.
• exposure: str, optional (default=None)
  • The exposure of the MRAgent. If mode is 'E', the exposure is the given disease.
• outcome: str, optional (default=None)
  • The outcome of the MRAgent. If mode is 'O', the outcome is the given disease.
• AI_key: str, optional (default=None)
  • The API key for the LLMs. Optional if running local LLM.
• model: str, 'MR' or 'MR_MOE', optional (default='MR')
  • MR methods in TwoSampleMR tool . 'MR' for the classical Mendelian randomization model. 'MR_MOE' for the Mendelian randomization model integrating a mixture-of-experts machine learning framework.
• num: int, optional (default=100)
  • The number of articles to be retrieved from PubMed.
• bidirectional: bool, optional (default=False)
  • Whether to perform bidirectional MR analysis.
• synonyms: bool, optional (default=True)
  • Whether to obtain synonyms for exposure and outcome.
• introduction: bool, optional (default=True)
  • Whether to print the introduction of the disease befor the MR.
• LLM_model: str, optional (default='gpt-4o')
  • The LLM model used in the MRAgent.
• gwas_token: str, optional (default=None)
  • The GWAS token for the OpenGWAS data.
• opengwas_model: str, 'csv' or 'online' optional (default='csv')
  • The model of the OpenGWAS data. 'csv' for the local OpenGWAS data. 'online' for the online OpenGWAS data.

Methods:

run(self, step=None)

• step: list, optional (default=None)
  • The step of the MRAgent. If step is None, the MRAgent will run all steps. If step is a list, the MRAgent will run the steps in the list.

Example:

Mendelian randomisation analysis using MRAgent to investigate exposures and outcomes associated with back pain.

agent = MRAgent(outcome='back pain', model='MR', LLM_model='gpt-4o',
                AI_key='xxxx', gwas_token='xxxx', bidirectional=True,
                introduction=True, num=300)
agent.run(step=[1, 2, 3, 4, 5, 6, 7, 8, 9])

"Causal Validation" mode

In the Causal Validation mode, users can directly input a pair of exposure and outcome, and the MRAgent independently carries out all steps of the Mendelian randomization study, providing a convenient and efficient report.

Run agent_workflow_OE.py to start the MRAgent in the "Causal Validation" mode:

Class

class MRAgentOE(self, mode='OE', exposure=None, outcome=None, AI_key=None, model='MR', bidirectional=False,
                 synonyms=True, introduction=True, LLM_model='gpt-4o', gwas_token=None)

Parameters:

• mode: str, 'OE', optional (default='OE')
  • The mode of the MRAgent. 'OE' for "Causal Validation" mode.
• exposure: str, optional (default=None)
  • The exposure of the MRAgent.
• outcome: str, optional (default=None)
  • The outcome of the MRAgent.
• AI_key: str, optional (default=None)
  • The API key for the LLMs. Optional if running local LLM.
• model: str, 'MR' or 'MR_MOE', optional (default='MR')
  • MR methods in TwoSampleMR tool . 'MR' for the classical Mendelian randomization model. 'MR_MOE' for the Mendelian randomization model integrating a mixture-of-experts machine learning framework.
• num: int, optional (default=100)
  • The number of articles to be retrieved from PubMed.
• bidirectional: bool, optional (default=False)
  • Whether to perform bidirectional MR analysis.
• synonyms: bool, optional (default=True)
  • Whether to obtain synonyms for exposure and outcome.
• introduction: bool, optional (default=True)
  • Whether to print the introduction of the disease befor the MR.
• LLM_model: str, optional (default='gpt-4o')
  • The LLM model used in the MRAgent.
• gwas_token: str, optional (default=None)
  • The GWAS token for the OpenGWAS data.
• opengwas_model: str, 'csv' or 'online' optional (default='csv')
  • The model of the OpenGWAS data. 'csv' for the local OpenGWAS data. 'online' for the online OpenGWAS data.

Methods:

run(self, step=None)

• step: list, optional (default=None)
  • The step of the MRAgent. If step is None, the MRAgent will run all steps. If step is a list, the MRAgent will run the steps in the list.

Example:

Example of using MRAgent to perform Mendelian randomization analysis on the causal relationship between 'osteoarthritis' and 'back pain'

agent = MRAgentOE(exposure='osteoarthritis', outcome='back pain',
                  AI_key='', LLM_model='gpt-4o',
                  model='MR', synonyms=False, bidirectional=True, introduction=False, gwas_token=mr_key)
agent.run(step=[1, 2, 3, 4, 5, 6, 7, 8, 9])

Experiments

1. step_1_test_out.py
2. step_1_test_SimCSE.py
3. step_2_test.py
4. step_3_test.py
5. step_5_test.py
6. step_9_test_out.py
7. step_9_test_SimCSE.py

Citation

TODO

License

Apache License 2.0

Other

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