Given the unprecedented rate of global aging, advancing aging research and drug discovery to support healthy and productive longevity is a pressing socioeconomic need. Holistic models of human and population aging that account for biomedical background, environmental context, and lifestyle choices are fundamental to address these needs, but integration of diverse data sources and large data sets into comprehensive models is challenging using traditional approaches. Recent advances in artificial intelligence and machine learning, and specifically multimodal transformer-based neural networks, have enabled the development of highly capable systems that can generalize across multiple data types. As such, multimodal transformers can generate systemic models of aging that can predict health status and disease risks, identify drivers, or breaks of physiological aging, and aid in target discovery against age-related disease. The unprecedented capacity of transformers to extract and integrate information from large and diverse data modalities, combined with the ever-increasing availability of biological and medical data, has the potential to revolutionize healthcare, promoting healthy longevity and mitigating the societal and economic impacts of global aging.

中文翻译：

---

多模态变压器及其在衰老和年龄相关疾病药物靶点发现中的应用


鉴于全球老龄化速度空前，推进衰老研究和药物发现以支持健康和高效的长寿是一项紧迫的社会经济需求。考虑生物医学背景、环境背景和生活方式选择的人类和人口老龄化整体模型是满足这些需求的基础，但使用传统方法将不同的数据源和大型数据集整合到综合模型中是具有挑战性的。人工智能和机器学习的最新进展，特别是基于多模态 transformer 的神经网络，使可以泛化多种数据类型的高性能系统的开发成为可能。因此，多模态转换器可以生成衰老的系统模型，这些模型可以预测健康状况和疾病风险，确定生理衰老的驱动因素或中断，并有助于发现与年龄相关的疾病的目标。变压器从大型和多样化的数据模式中提取和集成信息的能力达到前所未有的能力，再加上生物和医学数据的可用性不断增加，有可能彻底改变医疗保健，促进健康长寿，并减轻全球老龄化对社会和经济的影响。