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Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis.
A novel genetic system to detect protein–protein interactions
Stanley Fields and Ok-kyu Song · 1989
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Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry
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Prediction of protein-protein interactions using random decision forest framework
Xue-Wen Chen and Mei Liu · 2005
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Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences
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Computational prediction of protein–protein interactions
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Transient protein–protein interactions
Saliha Ece Acuner Ozbabacan, Hatice Billur Engin, Attila Gursoy, and Ozlem Keskin · 2011
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Heterogeneous data integration by tree-augmented naïve b ayes for protein-protein interactions prediction
Xiaotong Lin and Xue-wen Chen · 2013
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A method for predicting protein-protein interaction types
Yael Silberberg, Martin Kupiec, and Roded Sharan · 2014
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A highly efficient approach to protein interactome mapping based on collaborative filtering framework
Xin Luo, Zhuhong You, Mengchu Zhou, Shuai Li, Hareton Leung, Yunni Xia, and Qingsheng Zhu · 2015
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Cytonca: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks
Yu Tang, Min Li, Jianxin Wang, Yi Pan, and Fang-Xiang Wu · 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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Thomas N Kipf and Max Welling · 2016
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Modulation of protein–protein interactions for the development of novel therapeutics
Ioanna Petta, Sam Lievens, Claude Libert, Jan Tavernier, and Karolien De Bosscher · 2016
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Cnn-rnn: A unified framework for multi-label image classification
Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, and Wei Xu · 2016
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Deepppi: boosting prediction of protein–protein interactions with deep neural networks
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Seonwoo Min, Byunghan Lee, and Sungroh Yoon · 2017
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Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Tanlin Sun, Bo Zhou, Luhua Lai, and Jianfeng Pei · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Predicting protein–protein interactions through sequence-based deep learning
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey N Chiang, Zhihao Wu, and Xiaowei Ding · 2020
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Multi-label classification with label graph superimposing
Ya Wang, Dongliang He, Fu Li, Xiang Long, Zhichao Zhou, Jinwen Ma, and Shilei Wen · 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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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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The emerging trends of multi-label learning
Weiwei Liu, Haobo Wang, Xiaobo Shen, and Ivor W Tsang · 2021
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Learning unknown from correlations: Graph neural network for inter-novel-protein interaction prediction
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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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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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Multi-label image recognition with graph convolutional networks
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Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation
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Breaking the glass ceiling for embedding-based classifiers for large output spaces
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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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Cot: an efficient and accurate method for detecting marker genes among many subtypes
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Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction
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Protein–protein interaction prediction with deep learning: A comprehensive review
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