aristoteleo · github-actions · May 25, 2025
diff --git a/README.md b/README.md
@@ -46,6 +46,7 @@ This repository, autonomously updated daily by our **Pantheon** agent system, co
 - [2025.03 Preprint] [IAN: An Intelligent System for Omics Data Analysis and Discovery](https://www.biorxiv.org/content/10.1101/2025.03.06.640921v1)
 - [2025.03 Preprint] [PharmAgents: Building a Virtual Pharma with Large Language Model Agents](https://arxiv.org/abs/2503.22164)
 - [2025.03 Preprint] [CompBioAgent: An LLM-powered agent for single-cell RNA-seq data exploration](https://www.biorxiv.org/content/10.1101/2025.03.17.643771v1)
+- [2025.03 Nature Methods] [Large language model-based agents enhance biomedical research workflow](https://www.nature.com/articles/s41592-024-02354-y)
 
 ## Benchmarks
 
@@ -75,4 +76,4 @@ This repository, autonomously updated daily by our **Pantheon** agent system, co
 - [2024.09 Artificial Intelligence Review] [Large language models in medical and healthcare fields: applications, advances, and challenges](https://link.springer.com/article/10.1007/s10462-024-10921-0)
 - [2024.12 Nature Cancer] [How AI agents will change cancer research and oncology](https://www.nature.com/articles/s43018-024-00861-7)
 - [2025.02 The Lancet] [The rise of agentic AI teammates in medicine](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(25)00202-8/abstract)
-- [2025.03 Trends in Biotechnology] [Large language model for knowledge synthesis and AI-enhanced biomanufacturing](https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(25)00045-9#:~:text=LLMs%20empower%20metabolic%20models%20to,gain%20new%20insights%20and%20predictions)
+- [2025.03 Trends in Biotechnology] [Large language model for knowledge synthesis and AI-enhanced biomanufacturing](https://www.cell.com/trends/biotechnology/fulltext/S0167-7799(25)00045-9#:~:text=LLMs%20empower%20metabolic%20models%20to,gain%20new%20insights%20and%20predictions)
diff --git a/daily_reports/2025-05-25.md b/daily_reports/2025-05-25.md
@@ -0,0 +1,110 @@
+# The Applications of LLM-based Agents in Biology and Medicine
+
+The integration of Large Language Models (LLMs) in various fields of biology and medicine presents significant advancements, facilitating novel methodologies, improving research outcomes, and enhancing patient care. Below is a comprehensive list of research papers highlighting these applications.
+
+- **[Your futuristic assistant: a large language model for scientific research](https://arxiv.org/abs/2503.00096)**  
+  This paper explores LLMs in scientific research, discussing their role in data analysis, generating hypotheses, and literature reviews.
+
+- **[Large Language Models for Biomedical Research: Current Applications and Prospects](https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5)**  
+  A review of LLM applications in biomedical research, particularly in drug discovery, genomics, and personalized medicine.
+
+- **[Large language models: a potential catalyst for scientific discovery](https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(24)00482-1/fulltext)**  
+  Discusses LLMs' potential to revolutionize biology and medicine by streamlining data analysis and enhancing cross-disciplinary communication.
+
+- **[A systematic review of LLMs in biomedical text mining](https://www.biorxiv.org/content/10.1101/2025.03.11.642548v1)**  
+  This systematic review evaluates LLMs in biomedical text mining, assessing effectiveness and challenges.
+
+- **[AI in Health Care: The Journey Ahead](https://www.nature.com/articles/s41746-024-01083-y)**  
+  Discusses the impact of AI, particularly LLMs, on healthcare practices and biological research, examining current applications and future directions.
+
+- **[Molecular Linking of Protein Structure and Function via Representational Learning](https://arxiv.org/abs/2502.11211)**  
+  Introduces a machine learning framework linking protein structure and function, vital for biological and medical applications.
+
+- **[Prompt engineering methods for applied biological research using large language models](https://www.sciencedirect.com/science/article/pii/S2667102625000294)**  
+  Explores methods of prompt engineering to leverage LLMs for understanding biological processes and analyzing datasets.
+
+- **[Large language model-based agents enhance biomedical research workflow](https://www.nature.com/articles/s41592-024-02354-y)**  
+  Discusses LLM agents' integration in biomedical research stages, showcasing improvements in design, analysis, and literature review.
+
+- **[The potential of large language models in medical education: A survey](https://pubmed.ncbi.nlm.nih.gov/39122951/)**  
+  Examines LLMs in medical education, focusing on enhancing learning experiences and support for educators.
+
+- **[Applications of Large Language Models in Biological and Medical Research: A Review](https://pmc.ncbi.nlm.nih.gov/articles/PMC10802675/)**  
+  This review discusses the various applications of LLMs in biological and medical research, highlighting their role in data integration and ethical implications.
+
+- **[Harnessing Large Language Models for Drug Discovery: Transforming the Future of Biomedical Research](https://pubmed.ncbi.nlm.nih.gov/39435343/)**  
+  Discusses LLM integration in drug discovery processes, showcasing their role in literature analysis and optimizing chemical properties.
+
+- **[Artificial Intelligence in Clinical and Radiological Practice: A Systematic Review of the Literature](https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/)**  
+  Analyzes AI applications in clinical and radiological practices, assessing effectiveness and potential benefits.
+
+- **[Applications of Large Language Models in Biology and Medicine](https://link.springer.com/article/10.1007/s10462-024-10921-0)**  
+  Explores LLM integration in biology and medicine, discussing data analysis, drug discovery, and patient interaction.
+
+- **[Exploring the Role of LLM-Based Agents in Biological Research](https://pmc.ncbi.nlm.nih.gov/articles/PMC11080827/)**  
+  Discusses LLM agents' applications in biological research, including data analysis and hypothesis generation.
+
+- **[Harnessing Generative AI as a Tool for Drug Discovery and Precision Medicine](https://www.nature.com/articles/s41746-024-01258-7)**  
+  Outlines the use of generative AI in drug discovery, optimizing treatment options based on patient data.
+
+- **[The applications of LLM-based agents in biology and medicine](https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(23)00216-7/fulltext)**  
+  Discusses the role of LLM agents in drug discovery, patient care, and enhancing medical research.
+
+- **[Universal Language Models for Biomedical Text Mining](https://arxiv.org/html/2401.06775v2)**  
+  Reviews universal language models designed for biomedical text mining tasks and their impact on information retrieval.
+
+- **[AI in Healthcare: Natural Language Processing in a Clinical Setting](https://arxiv.org/abs/2501.06271)**  
+  Focuses on LLM applications in healthcare, emphasizing specific use cases improving patient care and streamlining processes.
+
+- **[Prompt Optimizer: Self-Supervised Learning for Prompt Engineering of Large Language Models in Biomedical Applications](https://arxiv.org/html/2409.04481)**  
+  Presents a framework optimizing prompt engineering for LLMs in biomedical contexts, enhancing their performance.
+
+- **[Applications of Large Language Model Agents in Bioinformatics: A Framework for Accelerating Biological Research](https://www.nature.com/articles/s41598-024-61124-0)**  
+  Discusses how LLM agents improve bioinformatics research by automating tasks and enhancing collaboration.
+
+- **[Large Language Model Predictions for Protein–Protein Interaction Networks](https://pmc.ncbi.nlm.nih.gov/articles/PMC10720782/)**  
+  Highlights LLM application in predicting protein-protein interactions and its significance in understanding biological systems.
+
+- **[Machine learning for biology and medicine: A comprehensive review](https://www.nature.com/articles/s41591-025-03727-2)**  
+  Reviews machine learning applications in biology and medicine, discussing transformative potential in research and clinical practices.
+
+- **[Application of large language models in the healthcare domain: a scoping review](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-025-02954-4)**  
+  Analyzes LLM applications in healthcare, focusing on patient interaction and decision support.
+
+- **[A large language model improves scientific writing and data analysis for biologists](https://www.nature.com/articles/s41591-024-03097-1)**  
+  Demonstrates LLM applications in improving scientific writing and data analysis in biology.
+
+- **[Application of deep learning algorithms in the identification of cancer biomarkers: A review](https://www.sciencedirect.com/science/article/pii/S093336572400126X)**  
+  Discusses deep learning algorithms' integration in identifying cancer biomarkers for improved diagnostic processes.
+
+- **[Linguistic Statistical Methods for Biomedical Discourse](https://pmc.ncbi.nlm.nih.gov/articles/PMC5264505/)**  
+  Discusses novel linguistic statistical methods to enhance understanding of biomedical literature.
+
+- **[The Role of Bioinformatics in the Development of Personalized Medicine](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640333/)**  
+  Explains bioinformatics' role in developing personalized medicine strategies tailored to individual profiles.
+
+- **[Applications of Artificial Intelligence in Medicine and Pharmacy: A Scoping Review](https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2825399)**  
+  Provides an overview of AI applications in clinical outcomes and healthcare processes.
+
+- **[An integrated LLM-based framework for predictive modeling of drug responses in cancer](https://www.medrxiv.org/content/10.1101/2024.07.03.24309779v1)**  
+  Introduces a framework utilizing LLMs for predictive modeling of cancer drug responses, improving personalized medicine approaches.
+
+- **[AI-driven biomarker discovery in health care: A survey of emerging trends](https://www.nature.com/articles/s41746-023-00958-w)**  
+  Reviews AI advancements in biomarker discovery and their implications for healthcare diagnostics and treatments.
+
+- **[A large language model for predicting protein–protein interactions](https://pmc.ncbi.nlm.nih.gov/articles/PMC10920625/)**  
+  Focuses on a large language model designed to predict protein-protein interactions and its contribution to drug discovery.
+
+- **[Embedding digital knowledge in protein design to facilitate machine learning](https://www.nature.com/articles/s41587-024-02534-3)**  
+  Examines advancements in protein engineering and design via the integration of machine learning techniques.
+
+- **[LLM-Based Agents for HIV Care: A User-Centric Design Approach](https://www.sciencedirect.com/science/article/pii/S2589004224009350)**  
+  Discusses user-centric design approaches of LLM agents in HIV care and their influence on patient interactions.
+
+- **[Applications of Large Language Models in Computational Biology and Medicine](https://pubmed.ncbi.nlm.nih.gov/39463859/)**  
+  Reviews advancements in LLM applications for tasks like protein structure prediction and patient data analysis. 
+
+- **[Meta-Learning for Drug Discovery: P-MetaNet](https://arxiv.org/abs/2409.00133)**  
+  Discusses a meta-learning framework for drug discovery and the integration of LLMs for improved predictions.
+
+This compilation covers a range of perspectives and studies underlining the pivotal role of LLMs in both biological research and medical practice, representing a noteworthy shift in how we approach these fields.