{"type":"ScholarlyArticle","title":"SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein–Protein Interaction Prediction","authors":[{"name":"Ziyuan Zhao","slug":"ziyuan-zhao","given":"Ziyuan","family":"Zhao","affiliation":"Institute for Infocomm Research (I 2 R) , A*STAR , Singapore; School of Computer Science and Engineering (SCSE) , Nanyang Technological University , Singapore"},{"name":"Peisheng Qian","slug":"peisheng-qian","given":"Peisheng","family":"Qian","affiliation":"Institute for Infocomm Research (I 2 R) , A*STAR , Singapore"},{"name":"Xulei Yang","slug":"xulei-yang","given":"Xulei","family":"Yang","affiliation":"Institute for Infocomm Research (I 2 R) , A*STAR , Singapore; * STAR , Singapore"},{"name":"Zeng Zeng","slug":"zeng-zeng","given":"Zeng","family":"Zeng","affiliation":"School of Microelectronics , Shanghai University , China"},{"name":"Cuntai Guan","slug":"cuntai-guan","given":"Cuntai","family":"Guan","affiliation":"School of Computer Science and Engineering (SCSE) , Nanyang Technological University , Singapore"},{"name":"Wai Leong Tam","slug":"wai-leong-tam","given":"Wai Leong","family":"Tam","affiliation":"Genome Institute of Singapore (GIS) , A"},{"name":"Xiaoli Li","slug":"xiaoli-li","given":"Xiaoli","family":"Li","affiliation":"Institute for Infocomm Research (I 2 R) , A*STAR , Singapore; School of Computer Science and Engineering (SCSE) , Nanyang Technological University , Singapore"}],"abstract":"Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods suffer from significant performance degradation under complex real-world scenarios due to various factors, e.g., label scarcity and domain shift. In this paper, we propose a self-ensembling multi-graph neural network (SemiGNN-PPI) that can effectively predict PPIs while being both efficient and generalizable. In SemiGNN-PPI, we not only model the protein correlations but explore the label dependencies by constructing and processing multiple graphs from the perspectives of both features and labels in the graph learning process. We further marry GNN with Mean Teacher to effectively leverage unlabeled graph-structured PPI data for self-ensemble graph learning. We also design multiple graph consistency constraints to align the student and teacher graphs in the feature embedding space, enabling the student model to better learn from the teacher model by incorporating more relationships. Extensive experiments on PPI datasets of different scales with different evaluation settings demonstrate that SemiGNN-PPI outperforms state-of-the-art PPI prediction methods, particularly in challenging scenarios such as training with limited annotations and testing on unseen data.","doi":"10.24963/ijcai.2023/554","url":"https://ijcai.org/proceedings/2023/554","pdf":"https://ijcai.org/proceedings/2023/0554.pdf","markdown":"https://ijcai.org/proceedings/2023/554.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/554","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":554,"pages":{"first":4984,"last":4992},"keywords":["Multidisciplinary Topics and Applications: MDA: Bioinformatics","Multidisciplinary Topics and Applications: MDA: Health and medicine","Machine Learning: ML: Semi-supervised learning","Machine Learning: ML: Sequence and graph learning"],"videos":[],"references":[{"position":1,"raw":"[Acuner Ozbabacan et al., 2011] Saliha Ece Acuner Ozbabacan, Hatice Billur Engin, Attila Gur- soy, and Ozlem Keskin. Transient protein-protein interactions. Protein engineering, design and selection, 24(9):635-648, 2011.","title":"Transient protein-protein interactions","authors":"Saliha Ece, Acuner Ozbabacan, Hatice Billur Engin, Attila Gursoy, Ozlem Keskin","year":"2011","venue":"Protein engineering, design and selection","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bekker and Davis, 2020] Jessa Bekker and Jesse Davis. Learning from positive and unlabeled data: A survey. Ma- chine Learning, 2020.","title":"Learning from positive and unlabeled data: A survey","authors":"Davis ; Bekker, Jessa Bekker, Jesse Davis","year":"2020","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Browne et al., 2007] Fiona Browne, Haiying Wang, Huiru Zheng, and Francisco Azuaje. Supervised statistical and machine learning approaches to inferring pairwise and module-based protein interaction networks. In IEEE Inter- national Symposium on BioInformatics and BioEngineer- ing, 2007.","title":"Supervised statistical and machine learning approaches to inferring pairwise and module-based protein interaction networks","authors":"Fiona Browne, Haiying Wang, Huiru Zheng, Francisco Azuaje","year":"2007","venue":"IEEE International Symposium on BioInformatics and BioEngineering","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen and Liu, 2005] Xue-Wen Chen and Mei Liu. Predic- tion of protein-protein interactions using random decision forest framework. Bioinformatics, 2005.","title":"Prediction of protein-protein interactions using random decision forest framework","authors":"Xue-Wen Chen, Mei Liu","year":"2005","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2019a] Muhao Chen, Chelsea J-T Ju, Guangyu Zhou, Xuelu Chen, Tianran Zhang, Kai-Wei Chang, Carlo Zaniolo, and Wei Wang. Multifaceted protein-protein in- teraction prediction based on siamese residual rcnn. Bioin- formatics, 35(14):i305-i314, 2019.","title":"Multifaceted protein-protein interaction prediction based on siamese residual rcnn","authors":"Chen","year":"2019","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2019b] Zhao-Min Chen, Xiu-Shen Wei, Peng Wang, and Yanwen Guo. Multi-label image recognition with graph convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5177-5186, 2019.","title":"Multi-label image recognition with graph convolutional networks","authors":"Chen","year":"2019","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Choi et al., 2019] Jaehoon Choi, Taekyung Kim, and Changick Kim. Self-ensembling with gan-based data aug- mentation for domain adaptation in semantic segmenta- tion. In Proceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 6830-6840, 2019.","title":"Self-ensembling with gan-based data augmentation for domain adaptation in semantic segmentation","authors":"Jaehoon Choi, Taekyung Kim, Changick Kim","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Du et al., 2017] Xiuquan Du, Shiwei Sun, Changlin Hu, Yu Yao, Yuanting Yan, and Yanping Zhang. Deepppi: boosting prediction of protein-protein interactions with deep neural networks. Journal of chemical information and modeling, 57(6):1499-1510, 2017.","title":"Deepppi: boosting prediction of protein-protein interactions with deep neural networks","authors":"Du","year":"2017","venue":"Journal of chemical information and modeling","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fields and Song, 1989] Stanley Fields and Ok-kyu Song. A novel genetic system to detect protein-protein interac- tions. Nature, 340(6230):245-246, 1989.","title":"Stanley Fields and Ok-kyu Song. A novel genetic system to detect protein-protein interactions","authors":"Song Fields","year":"1989","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Guo et al., 2008] Yanzhi Guo, Lezheng Yu, Zhining Wen, and Menglong Li. Using support vector machine com- bined with auto covariance to predict protein-protein in- teractions from protein sequences. Nucleic acids research, 2008.","title":"Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences","authors":"Guo","year":"2008","venue":"Nucleic acids research","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Guo et al., 2019] Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N Holtmann-Rice, Satyen Kale, Sashank Reddi, and Sanjiv Kumar. Breaking the glass ceiling for embedding-based classifiers for large output spaces. Ad- vances in Neural Information Processing Systems, 2019.","title":"Breaking the glass ceiling for embedding-based classifiers for large output spaces","authors":"Chuan Guo, Ali Mousavi, Xiang Wu, Satyen Daniel N Holtmann-Rice, Sashank Kale, Sanjiv Reddi, Kumar","year":"2019","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hashemifar et al., 2018] Somaye Hashemifar, Behnam Neyshabur, Aly A Khan, and Jinbo Xu. Predicting protein-protein interactions through sequence-based deep learning. Bioinformatics, 34(17):i802-i810, 2018.","title":"Predicting protein-protein interactions through sequence-based deep learning","authors":"Hashemifar","year":"2018","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ho et al., 2002] Yuen Ho, Albrecht Gruhler, Adrian Heil- but, Gary D Bader, Lynda Moore, Sally-Lin Adams, Anna Millar, Paul Taylor, Keiryn Bennett, Kelly Boutilier, et al. Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry. Nature, 415(6868):180-183, 2002.","title":"Systematic identification of protein complexes in saccharomyces cerevisiae by mass spectrometry","authors":"Yuen Ho, Albrecht Gruhler, Adrian Heilbut, D Gary, Lynda Bader, Sally-Lin Moore, Anna Adams, Paul Millar, Keiryn Taylor, Kelly Bennett, Boutilier","year":"2002","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Rep- resentations, 2016.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Laine and Aila, 2017] Samuli Laine and Timo Aila. Tem- poral ensembling for semi-supervised learning. In Inter- national Conference on Learning Representations, 2017.","title":"Temporal ensembling for semi-supervised learning","authors":"Laine, Aila ;, Samuli Laine, Timo Aila","year":"2017","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2018] Hang Li, Xiu-Jun Gong, Hua Yu, and Chang Zhou. Deep neural network based predictions of pro- tein interactions using primary sequences. Molecules, 23(8):1923, 2018.","title":"Deep neural network based predictions of protein interactions using primary sequences","authors":"Li","year":"2018","venue":"Molecules","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Lin and Chen, 2013] Xiaotong Lin and Xue-wen Chen. Heterogeneous data integration by tree-augmented naïve b ayes for protein-protein interactions prediction. Pro- teomics, 13(2):261-268, 2013.","title":"Heterogeneous data integration by tree-augmented naïve b ayes for protein-protein interactions prediction","authors":"Lin, Chen ;, Xiaotong Lin, Xue-Wen Chen","year":"2013","venue":"Proteomics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liu et al., 2021] Weiwei Liu, Haobo Wang, Xiaobo Shen, and Ivor W Tsang. The emerging trends of multi-label learning. IEEE transactions on pattern analysis and ma- chine intelligence, 44(11):7955-7974, 2021.","title":"The emerging trends of multi-label learning","authors":"Weiwei Liu, Haobo Wang, Xiaobo Shen, Ivor W Tsang","year":"2021","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lu et al., 2022] Yingzhou Lu, Chiung-Ting Wu, Sarah J Parker, Zuolin Cheng, Georgia Saylor, Jennifer E Van Eyk, Guoqiang Yu, Robert Clarke, David M Herrington, and Yue Wang. Cot: an efficient and accurate method for de- tecting marker genes among many subtypes. Bioinformat- ics Advances, 2022.","title":"Cot: an efficient and accurate method for detecting marker genes among many subtypes","authors":"Yingzhou Lu, Chiung-Ting Wu, Sarah J Parker, Zuolin Cheng, Georgia Saylor, Jennifer E Van Eyk, Guoqiang Yu, Robert Clarke, David M Herrington, Yue Wang","year":"2022","venue":"Bioinformatics Advances","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Luo et al., 2015] Xin Luo, Zhuhong You, Mengchu Zhou, Shuai Li, Hareton Leung, Yunni Xia, and Qingsheng Zhu. A highly efficient approach to protein interactome map- ping based on collaborative filtering framework. Scientific reports, 5(1):1-10, 2015.","title":"A highly efficient approach to protein interactome mapping based on collaborative filtering framework","authors":"Xin Luo, Zhuhong You, Mengchu Zhou, Shuai Li, Hareton Leung, Yunni Xia, Qingsheng Zhu","year":"2015","venue":"Scientific reports","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lv et al., 2021] Guofeng Lv, Zhiqiang Hu, Yanguang Bi, and Shaoting Zhang. Learning unknown from correla- tions: Graph neural network for inter-novel-protein inter- action prediction. In IJCAI International joint conference on artificial intelligence, 2021.","title":"Learning unknown from correlations: Graph neural network for inter-novel-protein interaction prediction","authors":"Guofeng Lv, Zhiqiang Hu, Yanguang Bi, Shaoting Zhang","year":"2021","venue":"IJCAI International joint conference on artificial intelligence","doi":null,"url":null,"internal_url":"/proceedings/2021/506"},{"position":22,"raw":"[Ma et al., 2022] Yuchen Ma, Yanbei Chen, and Zeynep Akata. Distilling knowledge from self-supervised teacher by embedding graph alignment. In 33rd British Machine Vision Conference. BMVA Press, 2022.","title":"Distilling knowledge from self-supervised teacher by embedding graph alignment","authors":"Yuchen Ma, Yanbei Chen, Zeynep Akata","year":"2022","venue":"33rd British Machine Vision Conference","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Margolin et al., 2006] Adam A Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Ric- cardo Dalla Favera, and Andrea Califano. Aracne: an al- gorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. In BMC bioinformatics, volume 7, pages 1-15. Springer, 2006.","title":"Aracne: an al-gorithm for the reconstruction of gene regulatory networks in a mammalian cellular context","authors":"Ilya Adam A Margolin, Katia Nemenman, Chris Basso, Gustavo Wiggins, Riccardo Dalla Stolovitzky, Andrea Favera, Califano","year":"2006","venue":"BMC bioinformatics","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Min et al., 2017] Seonwoo Min, Byunghan Lee, and Sun- groh Yoon. Deep learning in bioinformatics. Briefings in bioinformatics, 18(5):851-869, 2017.","title":"Seonwoo Min, Byunghan Lee, and Sungroh Yoon. Deep learning in bioinformatics","authors":"Min","year":"2017","venue":"Briefings in bioinformatics","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Petta et al., 2016] Ioanna Petta, Sam Lievens, Claude Lib- ert, Jan Tavernier, and Karolien De Bosscher. Modula- tion of protein-protein interactions for the development of novel therapeutics. Molecular Therapy, 2016.","title":"Modulation of protein-protein interactions for the development of novel therapeutics","authors":"Ioanna Petta, Sam Lievens, Claude Libert, Jan Tavernier, Karolien De Bosscher","year":"2016","venue":"Molecular Therapy","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Pio et al., 2020] Gianvito Pio, Michelangelo Ceci, Francesca Prisciandaro, and Donato Malerba. Ex- ploiting causality in gene network reconstruction based on graph embedding. Machine Learning, 2020.","title":"Exploiting causality in gene network reconstruction based on graph embedding","authors":"Gianvito Pio, Michelangelo Ceci, Francesca Prisciandaro, Donato Malerba","year":"2020","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Pio et al., 2022] Gianvito Pio, Paolo Mignone, Giuseppe Magazzù, Guido Zampieri, Michelangelo Ceci, and Clau- dio Angione. Integrating genome-scale metabolic mod- elling and transfer learning for human gene regulatory net- work reconstruction. Bioinformatics, 2022.","title":"Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction","authors":"Gianvito Pio, Paolo Mignone, Giuseppe Magazzù, Guido Zampieri, Michelangelo Ceci, Claudio Angione","year":"2022","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Qu and Hickey, 2022] Xiaodong Qu and Timothy J. Hickey. Eeg4home: A human-in-the-loop machine learning model for eeg-based bci. In Augmented Cognition, 2022.","title":"Eeg4home: A human-in-the-loop machine learning model for eeg-based bci","authors":"Xiaodong Qu, Timothy J Hickey","year":"2022","venue":"Augmented Cognition","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Silberberg et al., 2014] Yael Silberberg, Martin Kupiec, and Roded Sharan. A method for predicting protein-protein interaction types. PLoS One, 9(3):e90904, 2014.","title":"A method for predicting protein-protein interaction types","authors":"Yael Silberberg, Martin Kupiec, Roded Sharan","year":"2014","venue":"PLoS One","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Skrabanek et al., 2008] Lucy Skrabanek, Harpreet K Saini, Gary D Bader, and Anton J Enright. Computational pre- diction of protein-protein interactions. Molecular biotech- nology, 38(1):1-17, 2008.","title":"Computational prediction of protein-protein interactions","authors":"Lucy Skrabanek, K Harpreet, Gary D Saini, Anton J Bader, Enright","year":"2008","venue":"Molecular biotechnology","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Soleymani et al., 2022] Farzan Soleymani, Eric Paquet, Herna Viktor, Wojtek Michalowski, and Davide Spinello. Protein-protein interaction prediction with deep learning: A comprehensive review. Computational and Structural Biotechnology Journal, 2022.","title":"Protein-protein interaction prediction with deep learning: A comprehensive review","authors":"Farzan Soleymani, Eric Paquet, Herna Viktor, Wojtek Michalowski, Davide Spinello","year":"2022","venue":"Computational and Structural Biotechnology Journal","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Sun et al., 2017] Tanlin Sun, Bo Zhou, Luhua Lai, and Jian- feng Pei. Sequence-based prediction of protein protein in- teraction using a deep-learning algorithm. BMC bioinfor- matics, 18(1):1-8, 2017.","title":"Sequence-based prediction of protein protein interaction using a deep-learning algorithm","authors":"Tanlin Sun, Bo Zhou, Luhua Lai, Jianfeng Pei","year":"2017","venue":"BMC bioinformatics","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Szklarczyk et al., 2019] 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. String v11: protein- protein association networks with increased coverage, sup- porting functional discovery in genome-wide experimental datasets. Nucleic acids research, 2019.","title":"String v11: proteinprotein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","authors":"Damian Szklarczyk, Annika L Gable, David Lyon, Alexander Junge, Stefan Wyder, Jaime Huerta-Cepas, Milan Simonovic, T Nadezhda, John H Doncheva, Peer Morris, Bork","year":"2019","venue":"Nucleic acids research","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Tajbakhsh et al., 2020] Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey N Chiang, Zhihao Wu, and Xiaowei Ding. Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis, 63:101693, 2020.","title":"Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation","authors":"Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey N Chiang, Zhihao Wu, Xiaowei Ding","year":"2020","venue":"Medical Image Analysis","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Tang et al., 2015] Yu Tang, Min Li, Jianxin Wang, Yi Pan, and Fang-Xiang Wu. Cytonca: a cytoscape plugin for cen- trality analysis and evaluation of protein interaction net- works. Biosystems, 127:67-72, 2015.","title":"Cytonca: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks","authors":"Yu Tang, Min Li, Jianxin Wang, Yi Pan, Fang-Xiang Wu","year":"2015","venue":"Biosystems","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Tarvainen and Valpola, 2017] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results. Advances in neural information pro- cessing systems, 2017.","title":"Mean teachers are better role models: Weightaveraged consistency targets improve semi-supervised deep learning results","authors":"Valpola Tarvainen, Antti Tarvainen, Harri Valpola","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2016] Jiang Wang, Yi Yang, Junhua Mao, Zhi- heng Huang, Chang Huang, and Wei Xu. Cnn-rnn: A unified framework for multi-label image classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2285-2294, 2016.","title":"Cnn-rnn: A unified framework for multi-label image classification","authors":"Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, Wei Xu","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Wang et al., 2020] Ya Wang, Dongliang He, Fu Li, Xiang Long, Zhichao Zhou, Jinwen Ma, and Shilei Wen. Multi- label classification with label graph superimposing. In Proceedings of the AAAI Conference on Artificial Intelli- gence, pages 12265-12272, 2020.","title":"Multilabel classification with label graph superimposing","authors":"Ya Wang, Dongliang He, Fu Li, Xiang Long, Zhichao Zhou, Jinwen Ma, Shilei Wen","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wong et al., 2015] Leon Wong, Zhu-Hong You, Shuai Li, Yu-An Huang, and Gang Liu. Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel pr-lpq descriptor. In Interna- tional Conference on Intelligent Computing, 2015.","title":"Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel pr-lpq descriptor","authors":"Leon Wong, Zhu-Hong You, Shuai Li, Yu-An Huang, Gang Liu","year":"2015","venue":"International Conference on Intelligent Computing","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xu et al., 2019] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural net- works? In International Conference on Learning Rep- resentations, 2019.","title":"How powerful are graph neural networks","authors":"Xu","year":"2019","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xu et al., 2022] Kaixin Xu, Liyang Liu, Ziyuan Zhao, Zeng Zeng, and Veeravalli Bharadwaj. Object-aware self- supervised multi-label learning. In IEEE International Conference on Image Processing, 2022.","title":"Object-aware selfsupervised multi-label learning","authors":"Kaixin Xu, Liyang Liu, Ziyuan Zhao, Zeng Zeng, Veeravalli Bharadwaj","year":"2022","venue":"IEEE International Conference on Image Processing","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Yang et al., 2020] Fang Yang, Kunjie Fan, Dandan Song, and Huakang Lin. Graph-based prediction of protein- protein interactions with attributed signed graph embed- ding. BMC bioinformatics, 21(1):1-16, 2020.","title":"Graph-based prediction of proteinprotein interactions with attributed signed graph embedding","authors":"Fang Yang, Kunjie Fan, Dandan Song, Huakang Lin","year":"2020","venue":"BMC bioinformatics","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[You et al., 2020] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph con- trastive learning with augmentations. Advances in Neural Information Processing Systems, 33:5812-5823, 2020.","title":"Graph contrastive learning with augmentations","authors":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhang et al., 2019] Yijia Zhang, Qingyu Chen, Zhihao Yang, Hongfei Lin, and Zhiyong Lu. Biowordvec, im- proving biomedical word embeddings with subword infor- mation and mesh. Scientific data, 6(1):1-9, 2019.","title":"Biowordvec, improving biomedical word embeddings with subword information and mesh","authors":"Yijia Zhang, Qingyu Chen, Zhihao Yang, Hongfei Lin, Zhiyong Lu","year":"2019","venue":"Scientific data","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhang et al., 2021] Yabin Zhang, Haojian Zhang, Bin Deng, Shuai Li, Kui Jia, and Lei Zhang. Semi-supervised models are strong unsupervised domain adaptation learn- ers. arXiv preprint arXiv:2106.00417, 2021.","title":"Semi-supervised models are strong unsupervised domain adaptation learners","authors":"Zhang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Zhao et al., 2021] Ziyuan Zhao, Kaixin Xu, Shumeng Li, Zeng Zeng, and Cuntai Guan. Mt-uda: Towards unsu- pervised cross-modality medical image segmentation with limited source labels. In International Conference on Med- ical Image Computing and Computer-Assisted Interven- tion, pages 293-303. Springer, 2021.","title":"Mt-uda: Towards unsupervised cross-modality medical image segmentation with limited source labels","authors":"Ziyuan Zhao, Kaixin Xu, Shumeng Li, Zeng Zeng, Cuntai Guan","year":"2021","venue":"International Conference on Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Zhao et al., 2022] Ziyuan Zhao, Fangcheng Zhou, Kaixin Xu, Zeng Zeng, Cuntai Guan, and S Kevin Zhou. Le-uda: Label-efficient unsupervised domain adaptation for med- ical image segmentation. IEEE Transactions on Medical Imaging, 2022.","title":"Le-uda: Label-efficient unsupervised domain adaptation for medical image segmentation","authors":"Ziyuan Zhao, Fangcheng Zhou, Kaixin Xu, Zeng Zeng, Cuntai Guan, Kevin Zhou","year":"2022","venue":"IEEE Transactions on Medical Imaging","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}