来自中国科学院、东北农业大学、澳门大学与吉林大学的研究团队联合提出了一种用于解读单细胞 RNA-seq 数据的新型孪生聚类框架 scSiameseClu，能 够有效缓解表征坍塌问题，实现更清晰的细胞群体分类，为 scRNA-seq 数据的分析提供了强大的工具。 在生命科学的进程中，过去的重点常常放在「群体」水平。通过传统的普通转录组测序（Bulk RNA-Seq），我们能够得到群体细胞的平均基因表达，但 这意味着一些稀有细胞的特征可能被掩盖。如今，研究者越来越希望能听见「单个」细胞的声音。 单细胞 RNA 测序（scRNA-seq）正是这样一种革命性技术，它能在细胞群体的喧嚣中，捕捉单个细胞的全面遗传信息，从而揭示隐藏的复杂特征。为了 理解这些复杂的信息，需要进行一个关键环节——细胞聚类，根据基因表达的相似性将细胞归类，这一过程充满挑战。 scRNA-seq 数据存在高噪声、高稀疏性和高维度的特点，即使是目前最有效的图神经网络（GNNs）方法，也存在着「图构建不足」和「表征坍塌」的问 题。正如下图所示，无论是基于深度学习的 scNAME，还是基于图神经网络的 scGNN，其逐渐趋同的表征结果，意味着均存在不同 ...

Group 1 - The article discusses various innovative technologies showcased at the Data Expo, highlighting advancements in artificial intelligence and user interaction [1][22] - Xiaomi's AI assistant, Xiao Ai, integrates with smart devices to provide personalized services through voice commands, enhancing user experience [4] - JD.com's 3D product display technology allows users to interact with products in a virtual environment, improving online shopping experiences [8] Group 2 - Flexible display technology, introduced at the expo, offers design freedom and superior image quality, applicable in various devices from smartphones to medical equipment [13] - The 360 Smart Firewall utilizes AI to enhance network security by detecting threats and providing a user-friendly management interface [17] - Super microcomputers, a cutting-edge concept, leverage tiny computing units for efficient energy use and potential applications in health monitoring and environmental sensing [21] Group 3 - Big data genetic prediction technology analyzes genomic data to forecast individual health risks and responses to medications, promoting personalized healthcare [27]

Core Insights - The presentation by Associate Professor Zhang Shugang from Ocean University of China focuses on the construction and application of an intelligent protein computing system, highlighting breakthroughs in traditional protein research challenges [1][3][4]. Group 1: Traditional Challenges in Protein Research - Proteins play a crucial role in biological functions but face challenges such as high structural analysis costs, delayed functional annotation, and low efficiency in novel protein design [1][3]. - The demand for understanding complex protein characteristics has increased, necessitating innovative approaches to overcome these challenges [1][3]. Group 2: AI-Driven Innovations - The introduction of AI technologies has revolutionized protein research, exemplified by the awarding of the 2024 Nobel Prize in Chemistry for breakthroughs in AI-driven protein structure prediction and design [3][4]. - The intelligent protein computing system enables significant advancements in large-scale functional annotation, interaction prediction, and 3D structure modeling, providing new technical pathways for drug discovery and life system simulation [1][3]. Group 3: Key Breakthroughs in Protein Computing - The core tasks of intelligent protein computing include: 1. **Protein Structure Prediction**: AlphaFold's models have achieved unprecedented accuracy in predicting protein structures, with AlphaFold2 providing atomic-level precision and AlphaFold3 extending capabilities to predict interactions with various biomolecules [4][5]. 2. **Functional Annotation**: The team has developed methods to automate protein function annotation using deep learning, significantly increasing the scale of data processed and improving prediction accuracy [6][7]. 3. **Interaction Prediction**: A self-developed model has been created to enhance the prediction of protein interactions, achieving over 95% accuracy in specific applications [16][20]. 4. **Protein Design**: The potential for designing new proteins has been demonstrated, with innovative approaches being explored for applications in vaccine development and cancer treatment [22]. Group 4: Multiscale Modeling in Life Systems - The research emphasizes the importance of multiscale modeling in understanding complex life systems, integrating various biological scales from molecular to cellular levels [23]. - The team has proposed a comprehensive modeling framework that encompasses multiple research points, aiming for a holistic simulation of life systems [23].

Core Insights - Chen Runsheng is a pioneer in non-coding gene research and a participant in the Human Genome Project, which is one of the largest life science projects globally [1][2] - He emphasizes that the future of AI in China lies not in the quantity of chips but in the density of intelligent computing [1] Group 1: Contributions to Genomics - Under Chen's leadership, China became the sixth country globally to possess large-scale genome sequencing capabilities [2][6] - In 1999, China joined the International Human Genome Project, taking on the task of sequencing approximately 30 million base pairs of the human chromosome 3 short arm, which represented 1% of the entire project [6] - Chen's team innovated sequencing methods, completing their tasks two years ahead of schedule, demonstrating significant advancements in genomic research [6] Group 2: Discoveries in Non-Coding DNA - Chen discovered that only 2%-3% of the human genome encodes proteins, while 97% consists of non-coding sequences previously deemed "junk DNA" [6][7] - His team focused on these non-coding regions, leading to the identification of new disease-related loci, particularly in cancer research [7] Group 3: Open Science and Collaboration - Since 1993, Chen's team has established a comprehensive database of 640,000 non-coding molecular information, which they chose to share openly with the global scientific community [7] - Chen believes that science is a collective human contribution and emphasizes the importance of sharing research findings for the advancement of knowledge [7] Group 4: AI and Future Innovations - Chen has been involved in AI research since the late 1980s, applying artificial neural networks to predict coding genes [8] - His current work involves integrating traditional Chinese medicine data into AI models, aiming to create a platform that merges different medical perspectives [8] - He advocates for viewing AI not merely as a tool but as a new center for innovation, which could lead to more creative possibilities in research and development [8]