在生物科学与人工智能（AI）的交汇点上，一种新型研究工具——大型细胞模型（Large Cellular Model, LCM）正展现出其巨大的潜力和变革性影响。这一领域内的创新，如scBERT、Geneformer、scGPT、scFoundation和GeneCompass等模型，不仅为生物学家提供了前所未有的工具，还预示着AI将如何彻底重塑生物学研究的未来。
#### 大型语言模型与生物科学的融合
大型语言模型（LLM）在自然语言处理领域取得了显著的进展，其架构和算法如今也成功应用于生物科学领域。这些大型细胞模型（LCM）通过模仿LLM的结构，专为单细胞转录组学设计，为研究者提供了一种强大的工具，用于解析细胞的复杂性，揭示基因表达的模式，以及理解细胞功能和生物过程的动态。
#### 跨学科合作与技术创新
来自腾讯AI Lab、加州大学、多伦多大学、清华大学和中国科学院的研究团队，通过《Quantitative Biology》期刊的专访，分享了他们开发的LCM模型背后的创新思路和技术挑战。这些模型不仅展示了在细胞类型注释、新细胞类型发现、新标记基因识别等任务上的应用潜力，还揭示了在生物任务中的应用潜力，以及LCM对生物研究的变革性影响。
#### 面临的挑战与未来展望
在LCM的研究和开发过程中，面临着缩放规律问题和数据预训练的必要性等关键挑战。如何有效地处理大规模数据，以及如何在数据有限的情况下实现模型的迁移学习和零样本学习，成为研究者需要解决的重要问题。然而，通过深入探讨这些挑战，科学家们也看到了AI与生命科学融合的无限可能，期待AI能够帮助回答有关生命的关键问题，推动生物学研究的前沿发展。
#### 预测与展望
随着LCM技术的不断进步，未来的生物研究将更加依赖于AI的支持。通过提供更精确的细胞分析、基因表达预测以及疾病机制的理解，LCM有望加速新药物的发现和个性化医疗的发展。这一领域的研究不仅将推动生物科学的理论进步，还将对医疗健康、生物技术以及整个生命科学领域产生深远的影响。
在这一创新浪潮中，科学家们正以开放和合作的态度，共同探索AI与生物科学的无限可能，为人类带来更健康、更智能的未来。
英语如下：
### AI Biology Revolution: Experts Discuss the Future of Cell Research
At the intersection of biological sciences and artificial intelligence (AI), a new research tool – the Large Cellular Model (LCM) – is demonstrating its potential for transformative change. Innovations such as scBERT, Geneformer, scGPT, scFoundation, and GeneCompass, among others, are not only providing biologists with unprecedented tools but also hinting at how AI will fundamentally reshape the future of biological research.
#### Fusion of Large Language Models and Biology
Significant advancements in large language models (LLMs) in the field of natural language processing have now been successfully applied to biological sciences. These large cellular models (LCMs), designed specifically for single-cell transcriptomics, offer researchers a powerful tool to dissect cellular complexities, uncover patterns of gene expression, and understand the dynamics of cellular functions and biological processes.
#### Interdisciplinary Collaboration and Technological Innovation
Research teams from Tencent AI Lab, University of California, University of Toronto, Tsinghua University, and the Chinese Academy of Sciences, through interviews in the journal ‘Quantitative Biology’, shared their innovative approaches and technical challenges behind the development of LCM models. These models showcase potential applications in tasks such as cell type annotation, discovery of new cell types, and identification of new marker genes, as well as the transformative impact of LCMs on biological research.
#### Challenges and Future Prospects
In the development of LCMs, key challenges such as scaling laws and the necessity of data pre-training are being faced. How to effectively handle large-scale data and how to achieve model transfer learning and zero-shot learning with limited data are issues that researchers are working to solve. However, through this exploration, scientists see the potential for AI to revolutionize the integration with life sciences, with hopes that AI can help answer fundamental questions about life and drive the advancement of biological research.
#### Predictions and Outlook
As LCM technology advances, future biological research will increasingly rely on AI support. By providing more accurate cell analysis, gene expression predictions, and insights into disease mechanisms, LCMs are expected to accelerate the discovery of new drugs and the development of personalized medicine. This field of research not only promises to advance the theoretical progress in biological sciences but also has profound implications for healthcare, biotechnology, and the broader life sciences domain.
In this wave of innovation, scientists are approaching AI’s integration with biological sciences with an open and collaborative mindset, together exploring the limitless possibilities of AI and biology to bring about a healthier, smarter future for humanity.
【来源】https://www.jiqizhixin.com/articles/2024-07-25-7
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