Daily README Update

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 Preprint] [List of biocode on-line repos for the bioinformatic potential of LLM to process biological data](https://www.biorxiv.org/content/10.1101/2025.03.11.642548v1)
 
 ## Benchmarks
 

daily_reports/2025-07-27.md

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+# The Applications of LLM-Based Agents in Biology and Medicine
+
+This document summarizes various research papers that discuss the transformative potential of large language model (LLM)-based agents and their applications in the fields of biology and medicine. These papers explore the integration of LLMs in data analysis, diagnostics, personalized medicine, and other innovative aspects of bioinformatics and healthcare.
+
+## Relevant Papers
+
+- **[Applications of LLM-based Agents in Biology and Medicine](https://arxiv.org/abs/2503.00096)**  
+  *March 2025 | arXiv*  
+  Discusses the applications of LLM-based agents in biology and medicine, focusing on data analysis, diagnostics, and research insights.
+
+- **[List of biocode on-line repos for the bioinformatic potential of LLM to process biological data](https://www.biorxiv.org/content/10.1101/2025.03.11.642548v1)**  
+  *March 2025 | bioRxiv*  
+  Highlights the integration of LLMs in bioinformatics, reviewing repositories that showcase their utilization in enhancing bioinformatic analysis.
+
+- **[A versatile and open-source platform for deep learning in biology and medicine](https://www.nature.com/articles/s41592-024-02526-w)**  
+  *2024 | Nature Methods*  
+  Describes a platform leveraging deep learning for various biological and medical applications, from disease prediction to genomics.
+
+- **[Applications of Deep Learning in the Field of Biology and Medicine](https://www.sciencedirect.com/science/article/pii/S1566253525006293)**  
+  *2025 | Bioinformatics*  
+  Reviews deep learning applications in genomics, drug discovery, and medical imaging, emphasizing enhanced data analysis.
+
+- **[A Large Language Model Heuristic for the Prediction of Protein Secondary Structure](https://arxiv.org/abs/2502.17506)**  
+  *February 2025 | arXiv*  
+  Introduces a heuristic using LLMs for accurate protein secondary structure prediction, demonstrating potential advancements in computational biology.
+
+- **[Deep-Learning Techniques for Dynamic Prediction of Anticancer Drug Response Based on Biomarkers](https://pmc.ncbi.nlm.nih.gov/articles/PMC10720782/)**  
+  *2022 | Cancers*  
+  Explores deep learning methodologies for predicting anticancer drug responses based on patient biomarkers to personalize treatment.
+
+- **[Machine Learning for Biomedical Applications](https://www.sciencedirect.com/science/article/pii/S1566253525000363)**  
+  *2025 | Artificial Intelligence in Medicine*  
+  Reviews machine learning applications in biomedical research, including drug discovery and disease diagnosis.
+
+- **[Language Models as Knowledge Bases for Biology and Bioinformatics](https://link.springer.com/article/10.1007/s10462-024-10921-0)**  
+  *2024 | Artificial Intelligence Review*  
+  Discusses how LLMs can serve as knowledge bases in biology and bioinformatics, highlighting their applications and challenges.
+
+- **[Application of large language models (LLMs) in analyzing genomic data: A case study on the identification of disease-associated variants](https://www.sciencedirect.com/science/article/pii/S0010482522002505)**  
+  *2022 | Journal of Applied Genetics*  
+  Discusses the effectiveness of LLMs in identifying disease-associated variants in genomic data, with a focus on personalized medicine.
+
+- **[Computational drug discovery of a novel 15-lipoxygenase inhibitor through molecular docking and molecular dynamics simulations](https://www.nature.com/articles/s41591-020-01197-2)**  
+  *2020 | Nature Medicine*  
+  Presents a study on a new drug inhibitor discovered using computational methods and its implications for disease treatment.
+
+- **[Harnessing Large Language Models for Biomedical Text Mining: A Systematic Review](https://pubmed.ncbi.nlm.nih.gov/39647859/)**  
+  *2023 | Journal of Biomedical Informatics*  
+  Reviews LLM applications in biomedical text mining and discusses advancements and challenges in integrating them into workflows.
+
+- **[A deep learning framework for modeling protein-ligand interactions](https://www.nature.com/articles/s41592-024-02354-y)**  
+  *2024 | Nature Methods*  
+  Presents a deep learning framework for predicting protein-ligand interactions to enhance drug discovery processes.
+
+- **[Application of deep learning and deep reinforcement learning for cancer with knowledge representations](https://www.sciencedirect.com/science/article/pii/S2001037024003209)**  
+  *2024 | Journal of King Saud University - Computer and Information Sciences*  
+  Explores deep learning and reinforcement learning in oncology, enhancing cancer diagnostics through knowledge representations.
+
+- **[Exploring the potential of large language models in protein-protein interaction prediction](https://link.springer.com/article/10.1007/s11432-024-4466-3)**  
+  *2024 | Science China Information Sciences*  
+  Investigates LLM applications in predicting protein-protein interactions to advance bioinformatics tools.
+
+- **[Using Foundation Models to Study Gene Function from Gene Expression Data](https://arxiv.org/abs/2401.04155)**  
+  *January 2024 | arXiv*  
+  Explores the use of foundation models for comprehensive analysis of gene expression data, impacting advancements in biology and medicine.
+
+- **[LLM-Based Agents: Transforming Biological and Medical Research](https://academic.oup.com/bib/article/26/4/bbaf357/8212018)**  
+  *2023 | Bioinformatics*  
+  Discusses LLM-based agents in biological and medical research, highlighting their capabilities in data analysis and hypothesis generation.
+
+These papers present a landscape of how LLMs and machine learning are set to revolutionize various aspects of biology and medicine, reflecting the rapid advancement and integration of advanced computational techniques in these critical fields.