no code implementations • 23 Apr 2024 • Xingyue Zhao, Zhongyu Li, Xiangde Luo, Peiqi Li, Peng Huang, Jianwei Zhu, Yang Liu, Jihua Zhu, Meng Yang, Shi Chang, Jun Dong
Especially, an asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels, i. e., conservative labels and radical labels, to train two asymmetric segmentation networks simultaneously.
no code implementations • 8 Jun 2023 • Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu, Tie-Yan Liu
In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
1 code implementation • NeurIPS 2021 • He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu
These methods generally derive coevolutionary features by aggregating the learned residue representations from individual sequences with equal weights, which is inconsistent with the premise that residue co-evolutions are a reflection of collective covariation patterns of numerous homologous proteins.
no code implementations • 29 Oct 2021 • Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, Bin Shao, Tao Qin, Tie-Yan Liu
The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences.
no code implementations • 6 Jun 2019 • Fusong Ju, Jianwei Zhu, Guozheng Wei, Qi Zhang, Shiwei Sun, Dongbo Bu
Therefore, the traditional deep neural networks designed for image processing cannot be directly applied on sequence sets.
Multiple Sequence Alignment Protein Secondary Structure Prediction +1
1 code implementation • 31 Aug 2018 • Haicang Zhang, Qi Zhang, Fusong Ju, Jianwei Zhu, Yujuan Gao, Ziwei Xie, Minghua Deng, Shiwei Sun, Wei-Mou Zheng, Dongbo Bu
We further present successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset.