Citations 2
Large language models for bioinformatics
Ruan, Wei, Lyu, Yanjun , Zhang, Jing , Cai, Jiazhang , Lu, Yao , Wang, Tao , Liu, Yufang , Fang, Luyang , Liu, Ziyu , Liu, Zhengliang , Li, Yiwei , Wu, Zihao , Chen, Junhao , Jiang, Hanqi, Yang, Zhenyuan, Liang, Shizhe, Zhang, Wei , Ma, Terry, Dou, Yuan , Zhang, Jianli , Gong, Xinyu , Gan, Qi , Zou, Yusong , Chen, Z. J. , Qian, Yuanxin, Yu, Shuo , Song, Kenan, Sikora, Andrea, Li, Xiang , Wang, Yingfeng , Zhong, Wenxuan, Liu, Rongjie , Huang, Chao , Shen, Ye , Yan, Yajun , Liu, Tianming, Huang, Chao , Song, Kenan , Wang, Peilong, Chen, Jingyuan, Song, Kenan , Ruan, Wei, Shu, Peng , Yang, Ge , Gao, Shang , Wang, Yue , Wang, Peilong , Zhao, Lin , Jiang, Hanqi , Pan, Yi Feng , Chen, Jingyuan , Dou, Yuan, Chen, Z. J. , Qian, Yuanxin, Jin, Lu , Wang, Xianqiao , Sikora, Andrea , Li, Gang , Li, Quanzheng , Zhang, Lu, Abate, Yohannes , He, Lifang , Zhong, Wenxuan, Liu, Wei , Ma, Ping , Zhu, Hongtu , Zhu, Dajiang
Quantitative Biology | September 29, 2025 | Volume: 14 | Issue: 1 | Pages: No page available
Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.
SNX27-FERM-SNX1 complex structure rationalizes divergent trafficking pathways by SNX17 and SNX27
Yong, Xin, Ham, Hyoungjun , Liu, Zhe , Ren, Jie , Zhang, Zhen , Zhou, Yifei, Yang, Qin , Mo, Xianming , Billadeau, Daniel D. , Jia, Da , Yang, Qin , Mo, Xianming , Hu, Junjie , Jia, Da , Yong, Xin, Zhao, Lin , Hu, Wenfeng , Sun, Qingxiang
Proceedings of the National Academy of Sciences | August 30, 2021 | Volume: 118 | Issue: 36 | Pages: No page available
Significance The sorting nexin proteins are critical for endosomal trafficking and signaling. SNX17 and SNX27 are two SNX proteins displaying high similarity in domain structure, including a common FERM domain; however, they mediate distinct endocytic recycling pathways. We now solve this mystery by demonstrating that the FERM domains of SNX17 and SNX27 have different functions. Whereas the FERM domain of SNX17 recognizes the NPxY/NxxY motif, the same domain in SNX27 binds to a novel “DLF” motif within the N termini of SNX1/2. The interaction between SNX27 and SNX1/2 not only helps efficient retrieval of multiple cargoes but also promotes endosomal recruitment of SNX27. We further demonstrate that the SNX27–SNX1/2 interaction is crucial to neuronal growth and brain development in zebrafish.