Publications (15)
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Mingchen Li, Liqi Kang, Yi Xiong +4
Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functio…
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning
Ziyi Zhou, Liang Zhang, Yuanxi Yu +3
Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Recently, due to their capacity and representation ability, pre-trained protein l…
InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition
Meng Sang, Jiaxuan Chen, Mengzhen Li +4
In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative powe…
VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data
Liang Zhang, Hua Pang, Chenghao Zhang +11
In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (…
COMET: Benchmark for Comprehensive Biological Multi-omics Evaluation Tasks and Language Models
Yuchen Ren, Wenwei Han, Qianyuan Zhang +13
As key elements within the central dogma, DNA, RNA, and proteins play crucial roles in maintaining life by guaranteeing accurate genetic expression and implementation. Although res…
The minimum linear locality of linear codes
Pan Tan, Cuiling Fan, Cunsheng Ding +1
Locally recoverable codes (LRCs) were proposed for the recovery of data in distributed and cloud storage systems about nine years ago. A lot of progress on the study of LRCs has be…