Computational Drug-target Interaction Prediction based on Graph Embedding and Graph Mining
暂无分享,去创建一个
Vladimir B. Bajic | Haitham Ashoor | Magbubah Essack | Maha A. Thafar | Rawan S. Olayan | Somayah Albaradie | V. Bajic | M. Essack | H. Ashoor | Somayah Albaradie
[1] Yanli Wang,et al. Predicting drug-target interactions by dual-network integrated logistic matrix factorization , 2017, Scientific Reports.
[2] Joel Lexchin,et al. The cost of drug development: a systematic review. , 2011, Health policy.
[3] Fernando Nogueira,et al. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning , 2016, J. Mach. Learn. Res..
[4] Hao Ding,et al. Similarity-based machine learning methods for predicting drug-target interactions: a brief review , 2014, Briefings Bioinform..
[5] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[6] Jure Leskovec,et al. node2vec: Scalable Feature Learning for Networks , 2016, KDD.
[7] Yoshihiro Yamanishi,et al. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.
[8] Ping Zhang,et al. Interpretable Drug Target Prediction Using Deep Neural Representation , 2018, IJCAI.
[9] Jens Keilwagen,et al. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R , 2015, Bioinform..
[10] Yoshihiro Yamanishi,et al. Supervised prediction of drug–target interactions using bipartite local models , 2009, Bioinform..
[11] Susumu Goto,et al. SIMCOMP/SUBCOMP: chemical structure search servers for network analyses , 2010, Nucleic Acids Res..
[12] Qi Zhao,et al. Computational Model Development of Drug-Target Interaction Prediction: A Review. , 2019, Current protein & peptide science.
[13] Artem Cherkasov,et al. SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines , 2017, Journal of Cheminformatics.
[14] Manoj Kumar Gupta,et al. A comprehensive review of feature based methods for drug target interaction prediction , 2019, J. Biomed. Informatics.
[15] Jian Peng,et al. A Network Integration Approach for Drug-Target Interaction Prediction and Computational Drug Repositioning from Heterogeneous Information , 2017, RECOMB 2017.
[16] Vladimir B. Bajic,et al. Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities , 2019, Front. Chem..
[17] Abdollah Dehzangi,et al. CFSBoost: Cumulative feature subspace boosting for drug-target interaction prediction. , 2019, Journal of theoretical biology.
[18] Sampo Pyysalo,et al. Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches , 2018, BMC Bioinformatics.
[19] Ivan G. Costa,et al. A multiple kernel learning algorithm for drug-target interaction prediction , 2016, BMC Bioinformatics.
[20] Elena Marchiori,et al. Gaussian interaction profile kernels for predicting drug-target interaction , 2011, Bioinform..
[21] Kevin Chen-Chuan Chang,et al. A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications , 2017, IEEE Transactions on Knowledge and Data Engineering.
[22] Yanqing Niu,et al. Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction. , 2019, Current drug metabolism.
[23] Akira R. Kinjo,et al. Neuro-symbolic representation learning on biological knowledge graphs , 2016, Bioinform..
[24] Alexander E. Ivliev,et al. Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach , 2013, PloS one.
[25] Lukasz Kurgan,et al. Survey of Similarity-based Prediction of Drug-protein Interactions. , 2018, Current medicinal chemistry.
[26] Steven Skiena,et al. DeepWalk: online learning of social representations , 2014, KDD.
[27] Xiao-Ying Yan,et al. Prediction of Drug-Target Interaction with Graph Regularized Non-Negative Matrix Factorization , 2019 .
[28] Sudipta Pathak,et al. Ensemble learning algorithm for drug-target interaction prediction , 2017, ICCABS.
[29] Palash Goyal,et al. Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..
[30] Ping Zhang,et al. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions , 2017, J. Web Semant..
[31] Minoru Kanehisa,et al. KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..
[32] Hyeon-Eui Kim,et al. Deep mining heterogeneous networks of biomedical linked data to predict novel drug‐target associations , 2017, Bioinform..
[33] Yong Zhou,et al. Computational Methods for the Prediction of Drug-Target Interactions from Drug Fingerprints and Protein Sequences by Stacked Auto-Encoder Deep Neural Network , 2017, ISBRA.
[34] Abdollah Dehzangi,et al. iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting , 2017, Scientific Reports.
[35] Chee Keong Kwoh,et al. Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[36] Panos Kalnis,et al. DASPfind: new efficient method to predict drug–target interactions , 2016, Journal of Cheminformatics.
[37] Shuigeng Zhou,et al. Boosting compound-protein interaction prediction by deep learning , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[39] Hojung Nam,et al. SELF-BLM: Prediction of drug-target interactions via self-training SVM , 2017, PloS one.
[40] Chunyan Miao,et al. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction , 2016, PLoS Comput. Biol..
[41] Chee-Keong Kwoh,et al. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey , 2019, Briefings Bioinform..
[42] Krisztian Buza,et al. Drug-target interaction prediction with Bipartite Local Models and hubness-aware regression , 2017, Neurocomputing.
[43] Jun Sese,et al. Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences , 2018, Bioinform..
[45] Vladimir B. Bajic,et al. DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches , 2017, Bioinform..
[46] Michal Magid-Slav,et al. Identification of Common Biological Pathways and Drug Targets Across Multiple Respiratory Viruses Based on Human Host Gene Expression Analysis , 2012, PloS one.
[47] Ladislav Peska,et al. ALADIN: A New Approach for Drug-Target Interaction Prediction , 2017, ECML/PKDD.