（来源：中国妇女报） 转自：中国妇女报 / 人物小传 / 万蕊雪，西湖大学生命科学学院特聘研究员、博士生导师，取得多次"世界首次"的突破，荣获多项重磅 大奖。28岁时，她获得了2018年度青年科学家奖，是四位获奖者中唯一的中国籍科学家，也是唯一一位 女性。作为女性科学家，她积极分享经历、扶持后辈，以自身光芒激励更多青年女性勇敢投身科学 □ 中国妇女报全媒体记者 姚改改 搞科研是种什么感受？苦？累？枯燥？在万蕊雪看来，通通不是。"我觉得做科学研究是一件非常快乐 的事情。"此刻，她语调轻快，满心满眼都是对自己科研事业的热爱。 年少有为 攻克世界难题引全球关注 2018年12月，年仅28岁的万蕊雪获得2018年度青年科学家奖。她是四位获奖者中唯一的中国籍科学家， 也是唯一一名女性。因她在剪接体三维结构及RNA剪接方面的研究成果，当选为细胞及分子生物学类 别的胜出者。 什么是剪接体？什么是RNA剪接？为什么要研究这方面内容？研究成果对人类生命健康有何重要意 义？面对中国妇女报全媒体记者一连串的问题，万蕊雪坦言这些是既简单又难回答的问题。"简单是因 为我对它们太了解了，难的是如何用通俗易懂的语言让大众快速明白。" 万蕊 ...

Core Viewpoint - The article discusses the significance of long non-coding RNAs (lncRNAs) in human genetics, emphasizing their essential roles in cancer and development, despite being previously considered non-functional or "junk" DNA [2][4]. Group 1: Research Development - A new CRISPR-Cas13 based screening technology was developed to target RNA at a transcriptome scale, allowing for the identification of 778 essential lncRNAs across five human cell types [4]. - This research indicates that many lncRNAs play crucial roles in human biology, challenging the notion that they are merely non-functional sequences [4]. Group 2: Research Withdrawal - The authors of the study announced the retraction of their paper due to the discovery of unfiltered off-target sequences in their CRISPR library, which could have led to inaccurate results [6][8]. - Following the identification of potential off-targets, the authors conducted a re-analysis of their data and updated their experiments, which resulted in changes to the identified essential lncRNAs [9].

Core Viewpoint - The article discusses the development of LucaOne, a generalized biological foundation model that can simultaneously understand and process nucleic acids (DNA and RNA) and protein sequences, marking a significant advancement in the field of life sciences [4][26]. Group 1: Introduction to LucaOne - LucaOne is the world's first foundational model capable of unifying the understanding of nucleic acids and protein sequences, likened to a "DeepSeek" for life sciences [4]. - The model was pre-trained on sequences from 169,861 species, showcasing its ability to comprehend key biological principles such as the translation of DNA into proteins [4][16]. Group 2: Technical Aspects of LucaOne - The model utilizes a vocabulary of 39 "characters" to encode nucleotides and amino acids, allowing it to read both nucleic acids and proteins [13]. - It employs semi-supervised learning, integrating known biological annotations to enhance its understanding [14]. - LucaOne has 1.8 billion parameters and has been trained on 36.95 billion biological sequence "words," enabling it to extract deep, universal patterns from nucleic acid and protein sequences [16]. Group 3: Performance and Capabilities - LucaOne demonstrated an impressive ability to understand the central dogma of molecular biology without explicit instruction, outperforming specialized models in tasks involving DNA and protein sequence matching [18]. - The model excels in generating embeddings that accurately capture the biological significance of sequences, outperforming other models in clustering similar sequences [19]. - It has shown strong performance across seven challenging bioinformatics tasks, including species classification and protein stability prediction, often using simpler downstream networks compared to specialized models [20][24]. Group 4: Significance and Future Outlook - LucaOne provides a unified framework for understanding the two core molecular carriers of life, breaking down barriers between different molecular types [26]. - The model exemplifies the potential of foundational models in bioinformatics, allowing researchers to develop various biological computational tools efficiently [26]. - It paves the way for deeper and more automated analysis of complex biological systems, such as gene regulatory networks and disease mechanisms [26].