We propose an effective method to improve Protein Function Prediction (PFP) utilizing hierarchical features of Gene Ontology (GO) terms. Our method consists of a language model for encoding the protein sequence and a Graph Convolutional Network (GCN) for representing GO terms. To reflect the hierarchical structure of GO to GCN, we employ node(GO term)-wise representations containing the whole hierarchical information. Our algorithm shows effectiveness in a large-scale graph by expanding the GO graph compared to previous models. Experimental results show that our method outperformed state-of-the-art PFP approaches.
Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers
Banaras Hindu University, Varanasi, India（班纳拉锡大学）；Indiana University, Bloomington（印第安纳大学）；Indian Institute of Technology Jodhpur, India（印度理工学院朱罗尔分校）；Indian Institute of Technology Patna, India（印度理工学院帕坦分校）
Drug-Target Interaction Prediction via Hierarchical Sequential Cross-Attention over Chemical and Protein Language Models
I2A institute, TÉLUQ University（I2A研究所，TÉLUQ大学）；LIO, CRCHUM（LIO，蒙特利尔大学医院研究中心）；LCSI, ESI（LCSI，阿尔及尔国立高等信息学院）；CoMeT UR 24344, Universite de Poitiers（CoMeT UR 24344，普瓦捷大学）；University of Ottawa（渥太华大学）；Lebanese American University（黎巴嫩美国大学）
L^2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification
College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）；Inner Mongolia Key Laboratory of Mongolian Information Processing Technology（内蒙古蒙古语信息处理技术重点实验室）
HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction
School of Software Engineering Huazhong University of Science（软件工程学院 华中科技大学）；Faculty of Data Science City University of Macau Macau, China（数据科学学院 澳门城市大学）；Union Hospital, Tongji Medical College Huazhong University of Science（同济医学院附属医院 华中科技大学）
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
Zhejiang University（浙江大学）；McGill University（麦吉尔大学）；Westlake University（西湖大学）；AI Lab, Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心AI实验室）
Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
Department of Mathematics and Scientific Computing and Imaging (SCI) Institute University of Utah（数学与科学计算与成像研究所（SCI）大学）；Department of Mathematics, UCLA（数学系，加州大学洛杉矶分校）；Department of Mathematics, UC Davis（数学系，加州大学戴维斯分校）
