{"type":"ScholarlyArticle","title":"MLRDA: A Multi-Task Semi-Supervised Learning Framework for Drug-Drug Interaction Prediction","authors":[{"name":"Xu Chu","slug":"xu-chu","given":"Xu","family":"Chu","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Electronics Engineering and Computer Science , Peking University , Beijing , China"},{"name":"Yang Lin","slug":"yang-lin","given":"Yang","family":"Lin","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Electronics Engineering and Computer Science , Peking University , Beijing , China"},{"name":"Yasha Wang","slug":"yasha-wang","given":"Yasha","family":"Wang","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; National Engineering Research Center of Software Engineering , Peking University , Beijing , China"},{"name":"Leye Wang","slug":"leye-wang","given":"Leye","family":"Wang","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Electronics Engineering and Computer Science , Peking University , Beijing , China"},{"name":"Jiangtao Wang","slug":"jiangtao-wang","given":"Jiangtao","family":"Wang","affiliation":"School of Computing and Communications , Lancaster University , Lancashire , UK"},{"name":"Jingyue Gao","slug":"jingyue-gao","given":"Jingyue","family":"Gao","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education , Beijing , China; School of Electronics Engineering and Computer Science , Peking University , Beijing , China"}],"abstract":"Drug-drug interactions (DDIs) are a major cause of preventable hospitalizations and deaths. Recently, researchers in the AI community try to improve DDI prediction in two directions, incorporating multiple drug features to better model the pharmacodynamics and adopting multi-task learning to exploit associations among DDI types. However, these two directions are challenging to reconcile due to the sparse nature of the DDI labels which inflates the risk of overfitting of multi-task learning models when incorporating multiple drug features. In this paper, we propose a multi-task semi-supervised learning framework MLRDA for DDI prediction. MLRDA effectively exploits information that is beneficial for DDI prediction in unlabeled drug data by leveraging a novel unsupervised disentangling loss CuXCov. The CuXCov loss cooperates with the classification loss to disentangle the DDI prediction relevant part from the irrelevant part in a representation learnt by an autoencoder, which helps to ease the difficulty in mining useful information for DDI prediction in both labeled and unlabeled drug data. Moreover, MLRDA adopts a multi-task learning framework to exploit associations among DDI types. Experimental results on real-world datasets demonstrate that MLRDA significantly outperforms state-of-the-art DDI prediction methods by up to 10.3% in AUPR.","doi":"10.24963/ijcai.2019/628","url":"https://ijcai.org/proceedings/2019/628","pdf":"https://ijcai.org/proceedings/2019/0628.pdf","markdown":"https://ijcai.org/proceedings/2019/628.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/628","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning Applications","track":"Main track","number":628,"pages":{"first":4518,"last":4524},"keywords":["Machine Learning: Classification","Machine Learning: Semi-Supervised Learning","Machine Learning Applications: Bio;Medicine","Machine Learning: Deep Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Abdelaziz et al., 2017] Ibrahim Abdelaziz, Achille Fok- oue, Oktie Hassanzadeh, Ping Zhang, and Mohammad Sadoghi. Large-scale structural and textual similarity- based mining of knowledge graph to predict drugdrug in- teractions. Web Semant., 44(C):104-117, May 2017.","title":"Large-scale structural and textual similaritybased mining of knowledge graph to predict drugdrug interactions","authors":"Ibrahim Abdelaziz, Achille Fokoue, Oktie Hassanzadeh, Ping Zhang, Mohammad Sadoghi","year":"2017","venue":"Web Semant","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In ICLR, 2015.","title":"Neural machine translation by jointly learning to align and translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2015","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Caruana, 1997] Rich Caruana. Multitask learning. Machine learning, 28(1):41-75, 1997.","title":"Multitask learning","authors":"Caruana","year":"1997","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chang et al., 2018] Xiaobin Chang, Tao Xiang, and Timo- thy M Hospedales. Scalable and effective deep cca via soft decorrelation. In CVPR, 2018.","title":"Scalable and effective deep cca via soft decorrelation","authors":"Xiaobin Chang, Tao Xiang, Timothy M Hospedales","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheng and Zhao, 2014] Feixiong Cheng and Zhongming Zhao. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. Journal of the Ameri- can Medical Informatics Association Jamia, 21(2), 2014.","title":"Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties","authors":"Zhao Cheng, Feixiong Cheng, Zhongming Zhao","year":"2014","venue":"Journal of the American Medical Informatics Association Jamia","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cheung et al., 2015] Brian Cheung, Jesse A Livezey, Ar- jun K Bansal, and Bruno A Olshausen. Discovering hid- den factors of variation in deep networks. ICLR workshop, 2015.","title":"Discovering hidden factors of variation in deep networks","authors":"Brian Cheung, Jesse A Livezey, Arjun K Bansal, Bruno A Olshausen","year":"2015","venue":"ICLR workshop","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gao et al., 2019] Jingyue Gao, Xiting Wang, Yasha Wang, and Xing Xie. Explainable recommendation through at- tentive multi-view learning. In AAAI, 2019.","title":"Explainable recommendation through attentive multi-view learning","authors":"Jingyue Gao, Xiting Wang, Yasha Wang, Xing Xie","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Jin et al., 2017] Bo Jin, Haoyu Yang, Cao Xiao, Ping Zhang, Xiaopeng Wei, and Fei Wang. Multitask dyadic prediction and its application in prediction of adverse drug-drug interaction. In AAAI, 2017.","title":"Multitask dyadic prediction and its application in prediction of adverse drug-drug interaction","authors":"Bo Jin, Haoyu Yang, Cao Xiao, Ping Zhang, Xiaopeng Wei, Fei Wang","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kastrin et al., 2018] A. Kastrin, P. Ferk, and B. Leskošek. Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning. Plos One, 13(5):e0196865, 2018.","title":"Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning","authors":"A Kastrin, P Ferk, B Leskošek","year":"2018","venue":"Plos One","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Keskar et al., 2017] Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. On large-batch training for deep learning: Generalization gap and sharp minima. In ICLR, 2017.","title":"On large-batch training for deep learning: Generalization gap and sharp minima","authors":"Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak, Peter Tang","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kipf and Welling, 2017] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"Thomas N Kipf, Max Welling","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ma et al., 2018] Tengfei Ma, Cao Xiao, Jiayu Zhou, and Fei Wang. Drug similarity integration through attentive multi- view graph auto-encoders. In IJCAI, 2018.","title":"Drug similarity integration through attentive multiview graph auto-encoders","authors":"Tengfei Ma, Cao Xiao, Jiayu Zhou, Fei Wang","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Miyato et al., 2017] Takeru Miyato, Shin Ichi Maeda, Masanori Koyama, and Shin Ishii. Virtual adversarial training: a regularization method for supervised and semi- supervised learning. arXiv preprint arXiv:1704.03976, 2017.","title":"Virtual adversarial training: a regularization method for supervised and semisupervised learning","authors":"Miyato","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Percha and Altman, 2013] B Percha and R. B. Altman. In- formatics confronts drug-drug interactions. Trends in Pharmacological Sciences, 34(3):178-184, 2013.","title":"Informatics confronts drug-drug interactions","authors":"Altman ; B Percha, R B Percha, Altman","year":"2013","venue":"Trends in Pharmacological Sciences","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Qato et al., 2016] D. M. Qato, J Wilder, L. P. Schumm, V Gillet, and G. C. Alexander. Changes in prescription and over-the-counter medication and dietary supplement use among older adults in the united states, 2005 vs 2011. Jama Internal Medicine, 176(4):473, 2016.","title":"Changes in prescription and over-the-counter medication and dietary supplement use among older adults in the united states, 2005 vs 2011","authors":"D M Qato, L P Wilder, Schumm, G C Gillet, Alexander","year":"2016","venue":"Jama Internal Medicine","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Rasmus et al., 2015] Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko. Semi- supervised learning with ladder networks. In NeurlPS, 2015.","title":"Semisupervised learning with ladder networks","authors":"Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, Tapani Raiko","year":"2015","venue":"NeurlPS","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ryu et al., 2018] Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee. Deep learning improves predic- tion of drug-drug and drug-food interactions. PNAS, 115(18):E4304-E4311, 2018.","title":"Deep learning improves prediction of drug-drug and drug-food interactions","authors":"Jae Yong Ryu, Hyun Uk Kim, Sang Yup Lee","year":"2018","venue":"PNAS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Sun, 2013] Shiliang Sun. A survey of multi-view ma- chine learning. Neural Computing and Applications, 23(7- 8):2031-2038, 2013.","title":"A survey of multi-view machine learning","authors":"Shiliang Sun","year":"2013","venue":"Neural Computing and Applications","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Takeda et al., 2017] T Takeda, M. Hao, T. Cheng, S. H. Bryant, and Y. Wang. Predicting drug-drug interactions through drug structural similarities and interaction net- works incorporating pharmacokinetics and pharmacody- namics knowledge. Journal of Cheminformatics, 9(1):16, 2017.","title":"Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge","authors":"M Takeda, T Hao, S H Cheng, Y Bryant, Wang","year":"2017","venue":"Journal of Cheminformatics","doi":null,"url":null,"internal_url":null},{"position":21,"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. In NeurlPS, 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":"NeurlPS","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tatonetti et al., 2012] Nicholas P Tatonetti, P Ye Patrick, Roxana Daneshjou, and Russ B Altman. Data-driven pre- diction of drug effects and interactions. Science transla- tional medicine, 4(125):125ra31-125ra31, 2012.","title":"Data-driven prediction of drug effects and interactions","authors":"P Ye Nicholas P Tatonetti, Roxana Patrick, Russ B Daneshjou, Altman","year":"2012","venue":"Science translational medicine","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Vilar et al., 2012] Santiago Vilar, Rave Harpaz, Eugenio Uriarte, Lourdes Santana, Raul Rabadan, and Carol Fried- man. Drug-drug interaction through molecular structure similarity analysis. Journal of the American Medical In- formatics Association Jamia, 19(6):1066, 2012.","title":"Drug-drug interaction through molecular structure similarity analysis","authors":"Vilar","year":"2012","venue":"Journal of the American Medical Informatics Association Jamia","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Vilar et al., 2017] S Vilar, C Friedman, and G Hripcsak. De- tection of drug-drug interactions through data mining stud- ies using clinical sources, scientific literature and social media. Briefings in Bioinformatics, 2017.","title":"Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media","authors":"Vilar, G Friedman, Hripcsak","year":"2017","venue":"Briefings in Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhang et al., 2015] P. Zhang, F. Wang, J. Hu, and R Sor- rentino. Label propagation prediction of drug-drug inter- actions based on clinical side effects. Scientific Reports, 5:12339, 2015.","title":"Label propagation prediction of drug-drug interactions based on clinical side effects","authors":"P Zhang, F Wang, J Hu, Sorrentino","year":"2015","venue":"Scientific Reports","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang et al., 2017] Wen Zhang, Yanlin Chen, Feng Liu, Fei Luo, Gang Tian, and Xiaohong Li. Predicting potential drug-drug interactions by integrating chemical, biologi- cal, phenotypic and network data. Bmc Bioinformatics, 18(1):18, 2017.","title":"Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data","authors":"Wen Zhang, Yanlin Chen, Feng Liu, Fei Luo, Gang Tian, Xiaohong Li","year":"2017","venue":"Bmc Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zitnik et al., 2018] Marinka Zitnik, Monica Agrawal, and Jure Leskovec. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13):i457-i466, 2018.","title":"Modeling polypharmacy side effects with graph convolutional networks","authors":"Marinka Zitnik, Monica Agrawal, Jure Leskovec","year":"2018","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}