Computational Biology
Computational biology leverages computational tools and algorithms to analyze and interpret biological data, aiming to understand complex biological systems and processes. Current research heavily emphasizes the application of machine learning, particularly deep learning models like transformers and diffusion models, along with evolutionary algorithms and graph neural networks, to predict protein function, analyze gene expression, and solve other bioinformatics challenges. These advancements are significantly improving the speed, accuracy, and scalability of biological research, impacting areas such as drug discovery, personalized medicine, and disease understanding.
Papers
BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems
Nikita Mehandru, Amanda K. Hall, Olesya Melnichenko, Yulia Dubinina, Daniel Tsirulnikov, David Bamman, Ahmed Alaa, Scott Saponas, Venkat S. Malladi
Large Language Models for Bioinformatics
Wei Ruan, Yanjun Lyu, Jing Zhang, Jiazhang Cai, Peng Shu, Yang Ge, Yao Lu, Shang Gao, Yue Wang, Peilong Wang, Lin Zhao, Tao Wang, Yufang Liu, Luyang Fang, Ziyu Liu, Zhengliang Liu, Yiwei Li, Zihao Wu, Junhao Chen, Hanqi Jiang, Yi Pan, Zhenyuan Yang, Jingyuan Chen, Shizhe Liang, Wei Zhang, Terry Ma, Yuan Dou, Jianli Zhang, Xinyu Gong, Qi Gan, Yusong Zou, Zebang Chen, Yuanxin Qian, Shuo Yu, Jin Lu, Kenan Song, Xianqiao Wang, Andrea Sikora, Gang Li, Xiang Li, Quanzheng Li, Yingfeng Wang, Lu Zhang, Yohannes Abate, Lifang He, Wenxuan Zhong, Rongjie Liu, Chao Huang, Wei Liu, Ye Shen, Ping Ma, Hongtu Zhu, Yajun Yan, Dajiang Zhu, Tianming Liu