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Protein-Protein Interactions (PPIs) are fundamental in various biological processes and play a key role in life activities.
A novel genetic system to detect protein–protein interactions
Stanley Fields and Ok-kyu Song · 1989
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Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, et al · 1995
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Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry
Yuen Ho, Albrecht Gruhler, Adrian Heilbut, Gary D Bader, Lynda Moore, Sally-Lin Adams, Anna Millar, Paul Taylor, Keiryn Bennett, Kelly Boutilier, et al · 2002
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Deciphering protein–protein interactions. part i. experimental techniques and databases
Benjamin A Shoemaker and Anna R Panchenko · 2007
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Computational close up on protein–protein interactions: how to unravel the invisible using molecular dynamics simulations?
Christin Rakers, Marcel Bermudez, Bettina G Keller, Jérémie Mortier, and Gerhard Wolber · 2015
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The application of ligand-mapping molecular dynamics simulations to the rational design of peptidic modulators of protein–protein interactions
Yaw Sing Tan, David R Spring, Chris Abell, and Chandra S Verma · 2015
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Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel pr-lpq descriptor
Leon Wong, Zhu-Hong You, Shuai Li, Yu-An Huang, and Gang Liu · 2015
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Recent advances in protein-protein docking
Qian Zhang, Ting Feng, Lei Xu, Huiyong Sun, Peichen Pan, Youyong Li, Dan Li, and Tingjun Hou · 2016
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Application of machine learning approaches for protein-protein interactions prediction
Mengying Zhang, Qiang Su, Yi Lu, Manman Zhao, and Bing Niu · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Predicting protein–protein interactions through sequence-based deep learning
Somaye Hashemifar, Behnam Neyshabur, Aly A Khan, and Jinbo Xu · 2018
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Deep neural network based predictions of protein interactions using primary sequences
Hang Li, Xiu-Jun Gong, Hua Yu, and Chang Zhou · 2018
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Machine learning approaches for protein–protein interaction hot spot prediction: Progress and comparative assessment
Siyu Liu, Chuyao Liu, and Lei Deng · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Unified rational protein engineering with sequence-based deep representation learning
Ethan C Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M Church · 2019
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Multifaceted protein–protein interaction prediction based on siamese residual rcnn
Muhao Chen, Chelsea J-T Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo, and Wei Wang · 2019
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Machine-learning techniques for the prediction of protein–protein interactions
Debasree Sarkar and Sudipto Saha · 2019
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String v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets
Damian Szklarczyk, Annika L Gable, David Lyon, Alexander Junge, Stefan Wyder, Jaime Huerta-Cepas, Milan Simonovic, Nadezhda T Doncheva, John H Morris, Peer Bork, et al · 2019
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Predicting protein-protein interactions from matrix-based protein sequence using convolution neural network and feature-selective rotation forest
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Graphmae: Self-supervised masked graph autoencoders
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang · 2022
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What to hide from your students: Attention-guided masked image modeling
Ioannis Kakogeorgiou, Spyros Gidaris, Bill Psomas, Yannis Avrithis, Andrei Bursuc, Konstantinos Karantzalos, and Nikos Komodakis · 2022
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Semmae: Semantic-guided masking for learning masked autoencoders
Gang Li, Heliang Zheng, Daqing Liu, Chaoyue Wang, Bing Su, and Changwen Zheng · 2022
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Machine learning-aided engineering of hydrolases for pet depolymerization
Hongyuan Lu, Daniel J Diaz, Natalie J Czarnecki, Congzhi Zhu, Wantae Kim, Raghav Shroff, Daniel J Acosta, Bradley R Alexander, Hannah O Cole, Yan Zhang, et al · 2022
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Lei Wang, Hai-Feng Wang, San-Rong Liu, Xin Yan, and Ke-Jian Song · 2019
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Current experimental methods for characterizing protein–protein interactions
Mi Zhou, Qing Li, and Renxiao Wang · 2019
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Language modelling for biological sequences–curated datasets and baselines
Jose Juan Almagro Armenteros, Alexander Rosenberg Johansen, Ole Winther, and Henrik Nielsen · 2020
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Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, et al · 2020
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Self-supervised contrastive learning of protein representations by mutual information maximization
Amy X Lu, Haoran Zhang, Marzyeh Ghassemi, and Alan Moses · 2020
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Graph-based prediction of protein-protein interactions with attributed signed graph embedding
Fang Yang, Kunjie Fan, Dandan Song, and Huakang Lin · 2020
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A survey on computational models for predicting protein–protein interactions
Lun Hu, Xiaojuan Wang, Yu-An Huang, Pengwei Hu, and Zhu-Hong You · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei · 2022
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Adversarial masking for self-supervised learning
Yuge Shi, N Siddharth, Philip Torr, and Adam R Kosiorek · 2022
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Protein–protein interaction prediction methods: from docking-based to ai-based approaches
Yuko Tsuchiya, Yu Yamamori, and Kentaro Tomii · 2022
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Masked inverse folding with sequence transfer for protein representation learning
Kevin K Yang, Niccolò Zanichelli, and Hugh Yeh · 2022
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Structure-aware protein–protein interaction site prediction using deep graph convolutional network
Qianmu Yuan, Jianwen Chen, Huiying Zhao, Yaoqi Zhou, and Yuedong Yang · 2022
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2022
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Protein representation learning via knowledge enhanced primary structure reasoning
Hong-Yu Zhou, Yunxiang Fu, Zhicheng Zhang, Bian Cheng, and Yizhou Yu · 2022
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Protein-protein interaction studies using molecular dynamics simulation
Veerendra Kumar and Shivani Yaduvanshi · 2023
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Functional-group-based diffusion for pocket-specific molecule generation and elaboration
Haitao Lin, Yufei Huang, Haotian Zhang, Lirong Wu, Siyuan Li, Zhiyuan Chen, and Stan Z Li · 2023
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Quantifying the knowledge in gnns for reliable distillation into mlps
Lirong Wu, Haitao Lin, Yufei Huang, and Stan Z Li · 2023
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Ziyuan Zhao, Peisheng Qian, Xulei Yang, Zeng Zeng, Cuntai Guan, Wai Leong Tam, and Xiaoli Li · 2023
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Psc-cpi: Multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction
Lirong Wu, Yufei Huang, Cheng Tan, Zhangyang Gao, Haitao Lin, Bozhen Hu, Zicheng Liu, and Stan Z Li · 2024
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