HealthX Club于4月三个周末组织BioAI and Precision Health主题活动，邀请MD Anderson癌症研究中心、斯坦福大学等顶级研究中心一线研究人员，国内外投资机构投资人、产业界研发专家，围绕生物人工智能（BioAI）和精准医疗（Precision Health）进行深入探讨。
为什么要办这个活动？
出于对医疗科技的兴趣，我们参加过很多「xx大会」、「xx高峰论坛」，感觉虽然讲者很牛、内容很高大上、但是有的时候发现连概念都不太清楚，被一些big words吓坏惊呆，听完之后感觉到来赶了个场子，但是不知道到底对于自己认识癌症、衰老、临床AI等医疗科技有多少帮助。
所以！我们2020年成立了HealthX医疗科技俱乐部，只分享前沿干货、不浪费彼此时间，自己学到最干货最前沿的知识，有务实且革命性的启发。这是我们三年来做俱乐部一直的追求，我们不仅分享知识，组织talk，我们还进行了具体的行动，三年来我们通过俱乐部深度参与了十余项临床+AI、癌症+AI的交叉项目，并正在积极尝试转化。这次BioAI and Precision Health主题活动是我们对生物多组学数据+人工智能在精准医疗中的一次大胆尝试，希望务实、深入的学习相关底层技术和前沿科研进展，并迅速跟上，和我们的会员一起做出成果，探索落地的可能！
内容内容
「建立基于微量血样多组学数据的精准健康监测平台」斯坦福大学博士后申小涛博士
「单细胞测序深度刻画肿瘤免疫微环境」新格元市场总监周运来
「运用几何深度学习技术和以临床医生为中心的设计对治疗应用进行零样本学习」斯坦福大学在读博士黄柯鑫
「基于图神经网络的多模态单细胞数据整合」密西根州立大学在读博士Jiayuan Ding
「2022年生物医疗科技最新进展和2023年展望」HealthX Club 创始人周介立
「数字医疗创新发展报告解读」远毅资本投资经理侯占才
「生物AI赋能精准医疗最新科技突破」前美国MD Anderson癌症研究中心博士后研究员
读书会活动「基因组学与精准医疗101」HealthX团队
主题：《建立基于微量血样多组学数据的精准健康监测平台》内容简介：目前的医疗保健实践是被动的，使用有限的生理和临床信息，通常相隔数月或数年收集。此外，在临床和研究环境中发现和分析血液生物标志物受到地理障碍、临床静脉穿刺的成本和不便、低采样频率和低分子测量深度的限制。在这里，我们描述了一种策略，用于在10微升的血液中频繁捕获和分析数千种代谢物、脂质、细胞因子和蛋白质，以及来自可穿戴传感器的生理信息。我们在两个应用中展示了这种频繁和密集的多组学微采样的优势，即评估对复杂混合饮食干预的反应，以发现个性化的炎症和代谢反应；以及深度个性化分析，以揭示大规模的分子波动以及与日内生理变化（例如心率）以及临床生物标志物（特别是葡萄糖和皮质醇）和身体活动水平相关的数千种分子关系。预习内容请看公众号文章《自然生物医学工程：基于微量血样多组学数据的精准健康监测平台》
个人简介：自2018年起从事单细胞生物信息分析工作，拥有丰富的单细胞多组学数据挖掘经验。致力于单细胞技术应用推广，曾主理《单细胞时代》、《单细胞新药研发导论》等新媒体专栏。加入新格元后负责单细胞产品在科学研究和临床方向上的应用场景开发、单细胞平台方案建设。先后推出单细胞动态转录组、单细胞多样同测试剂盒、单细胞肺癌靶向基因突变检测试剂盒等六款单细胞相关试剂盒和PythoN组织解离仪。
内容简介：《2022数字医疗创新发展报告》是由远毅资本联合中国医药教育协会以及数十位行业专家发布的行业首部研究数字医疗创新的全面报告。报告从数字医疗产业整体演进的角度出发，阐释了由数字技术赋能的新兴医疗健康产业，从医疗信息化阶段、到互联网医疗阶段再到数字医疗创新阶段的突破性发展历程及重要节点，并解释了数字医疗的的基本定义与特征。报告解读将阐释数字医疗产业创新趋势，包括诊疗全流程创新、医院管理创新、医疗支付创新、药械企业创新，并对全球数字医疗投融资趋势做出分析。个人简介：侯占才，远毅资本投资经理，关注前沿生命科技和器械以及新兴数字医疗领域的投资机会。数字医疗公众号Boom Health主编，《2022数字医疗创新发展报告》执行主编，长期研究新兴医疗产业，对于前沿医疗技术及商业化运营有深刻理解。伦敦大学金融硕士，南京大学化学学士。
主题：《Graph Neural Networks in Single-Cell Analysis》内容简介：Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, Graph Neural Networks (GNNs) often demonstrate superior performance compared to traditional machine learning methods. In this talk, I will introduce how to leverage GNNs to resolve problems in multi-modal integration and spatial transcriptomics. What's more, I will introduce the DANCE platform, the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. https://github.com/OmicsML/dance.个人简介： I'm a Ph.D. student in the Department of Computer Science and Engineering at Michigan State University. I am fortunately advised by Prof. Jiliang Tang in the DSE lab. At the same time, I am a full-time software engineer (machine learning) in VMware in bay area. My research interests include but not limited to Graph Neural Networks, NLP and its applications in biology like single-cell analysis.
主题：《Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design》内容简介：Of the several thousand diseases that affect humans, only about 500 have treatments approved by the U.S. Food and Drug Administration. Even for those with approved treatments, discovering new drugs can offer alternative options that cause fewer side effects and replace drugs that are ineffective for certain patient groups. However, identifying new therapeutic opportunities for diseases with limited treatment options remains a challenge, as existing algorithms often perform poorly. Here, we leverage recent advances in geometric deep learning and human-centered AI to introduce TXGNN, a model for identifying therapeutic opportunities for diseases with limited treatment options and minimal molecular understanding. TXGNN is a graph neural network pre-trained on a comprehensive knowledge graph of 17,080 clinically-recognized diseases and 7,957 therapeutic candidates. The model can process various therapeutic tasks, such as indication and contraindication prediction, in a unified formulation. Once trained, we show that TXGNN can perform zero-shot inference on new diseases without additional parameters or fine-tuning on ground truth labels. Evaluation of TXGNN shows significant improvements over existing methods, with up to 49.2% higher accuracy in indication tasks and 35.1% higher accuracy in contraindication tasks. TXGNN can also predict therapeutic use for new drugs developed since June 2021. To facilitate interpretation and analysis of the model’s predictions by clinicians, we develop a human-AI explorer for TXGNN and evaluate its usability with medical experts. Finally, we demonstrate that TXGNN’s novel predictions are consistent with off-label prescription decisions made by clinicians in a large healthcare system. These label-efficient and clinician-centered learning systems pave the way for improvements for many therapeutic tasks.个人介绍：Kexin Huang is a 2nd-year CS PhD student at Stanford, advised by Prof. Jure Leskovec. His research focuses on algorithmic challenges arising from machine learning adoption in biomedicine. His research has published at Nature Biomedical Engineering, Nature Chemical Biology, NeurIPS, UAI, IEEE VIS etc. He had spent time researching at Pfizer, IQVIA, Dana-Farber, Flatiron Health, and Rockefeller University and received undergraduate education at NYU in math & CS & studio art, and master at Harvard in health data science.嘉宾：HealthX Club成立人、上海交通大学在读博士周介立主题: 「2022年生物医疗科技最新进展和2023年展望」个人简介：美国卡耐基梅隆大学机器学习和人工智能本科、硕士，前硅谷人工智能上市公司数据科学家，上海交通大学生物医疗人工智能在读博士，HealthX Club成立人，专注于AI for Bio and Health.俱乐部系列活动于上周末4/15号已经开始，目前还有两场活动于本周六和下周末进行，BioAI and Precision Health系列活动收费168元，包含整个系列的线上参与和内容整理，请付费后添加俱乐部运营人员获得参会方式。Step 2: 添加俱乐部运营微信，发送付款截图获得参会方式注：为了表示对早期VIP会员支持的感谢，本次活动以及2023年内所有会员活动，面向HealthX Club VIP会员免费参加*