Artificial intelligence-guided strategies for next-generation biological sequence design  

作　　者：Pengcheng Zhang Lei Wei Jiaqi Li Xiaowo Wang 

机构地区：[1]Ministry of Education Key Laboratory of Bioinformatics,Center for Synthetic and Systems Biology,Bioinformatics Division at the Beijing National Research Center for Information Science and Technology,Department of Automation,Tsinghua University,China

出　　处：《National Science Review》2024年第11期11-14,共4页国家科学评论（英文版）

基　　金：supported by the National Natural Science Foundation of China(62250007 and 62225307);the National Key R&D Program of China(2020YFA0906900 and 2023YFF1204500);the Beijing Municipal Natural Science Foundation(Z230015).

摘　　要：Recent advancements in artificial intelli-gence(AI)have revolutionized our abil-ity to model biological sequences,paving the way for a new AI-driven paradigm in next-generation biological sequence de-sign.In this article,we introduce how AI is utilized for conducting digital experi-ments,navigating the vast sequence land-scape and elucidating the intricate con-nections between sequence and function through advanced generative and predic-tive modeling techniques.Additionally,we discuss the adoption of active learn-ing approaches to bridge the gap between digital simulations and wet-lab experi-ments,thereby significantly improving ef-ficiency in testing the most informative data to elevate the performance of AI models.

关 键 词：thereby utilized CONDUCTING 

分 类 号：O17[理学—数学]