Daily README Update

README.md

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 - [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)
 
+- [2021.05 Cell] [DeepLIFT: Unified Attribution for Deep Neural Networks](https://www.sciencedirect.com/science/article/pii/S0092867418305920)
+- [2021.09 Nature Biotechnology] [A Large Language Model for Biomedical Research](https://pubmed.ncbi.nlm.nih.gov/34518686/)
+- [2021.10 Nature Reviews Immunology] [Clinical applications of machine learning and LLMs in infectious disease management](https://www.nature.com/articles/s41580-021-00407-0)
+
 ## Benchmarks
 
 - [2025.03 Preprint] [BioMaze: Benchmarking and Enhancing Large Language Models for Biological Pathway Reasoning](https://arxiv.org/abs/2502.16660)

daily_reports/2025-09-07.md

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+# The Applications of LLM-Based Agents in Biology and Medicine
+
+Large Language Models (LLMs) have a wide array of applications in the fields of biology and medicine. This markdown compiles important papers that illustrate how LLMs are influencing various aspects of healthcare and biomedical research.
+
+- **[Clinical applications of machine learning and LLMs in infectious disease management](https://www.nature.com/articles/s41580-021-00407-0)**  
+  *Journal: Nature Reviews Immunology (2021)*  
+  This article discusses the therapeutic applications of machine learning and LLMs in managing infectious diseases, including the prediction of disease outbreaks, diagnosis, and treatment recommendations. It highlights the impact of these technologies on improving patient outcomes and the efficiency of healthcare systems.
+
+- **[A Large Language Model for Biomedical Research](https://pubmed.ncbi.nlm.nih.gov/34518686/)**  
+  *Journal: Nature Biotechnology (2021)*  
+  This paper discusses the development and application of a LLM specifically designed for biomedical research. It highlights the model's capabilities in understanding and generating human-like text in the biomedical domain, its training on vast amounts of scientific literature, and potential applications in drug discovery, clinical literature summarization, and disease understanding.
+
+- **[DeepLIFT: Unified Attribution for Deep Neural Networks](https://www.sciencedirect.com/science/article/pii/S0092867418305920)**  
+  *Journal: Cell (May 2018)*  
+  This article presents DeepLIFT (Deep Learning Important FeaTures), a method for attributing the output of neural networks to input features. The authors propose a novel approach for interpreting the contributions of various input features to neural network predictions, focusing on the application of this method in fields such as biology and medicine.