菲利普·鲍尔/文 20多年过去了，事实表明，人类基因组计划联盟提供的信息，以及后续对数万个人类个体基因组的测序 结果，正是生物医学研究的重要资源。当然，这就是人类基因组计划一直以来的愿景和重要任务。这些 信息不仅让我们距了解生命本身这个目标稍近了一点儿，更是在某些方面向我们证明：人类与真正实现 这个目标之间的距离比我们预想的远得多。因为，即便真的存在某种类似生命语言的东西，我们也不可 能在基因组中找到——基因组与人类制作的任何说明书都不一样。 然而，这类带有误导性的基因组比喻一如既往地流行。其中，"蓝图"的比喻是流传最广的，其背后的含 义是：这30亿个字符串构成的"密码"中蕴含着构筑人体的方案，我们只要知道如何破译密码就好了。 实际上，"密码"这个概念本身就暗示基因组类似计算机程序，是生命运行的某种神秘算法。我们甚至赋 予"生命之书"这个比喻更为具体的物理含义：基因组由总共109本不同的书构成，分为23卷（每一卷代 表一对人类染色体），每一页上都密密麻麻地写着由4个字母（A，T，C，G）按各种顺序组成的文 字，这4个字母就是构成DNA的基本组分。我很乐意让读者自行判断哪本书——你正在读的这本或图中 的那本—— ...

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]