AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
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[1] Sumanta Ray,et al. DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation , 2020, PloS one.
[2] Parvin Razzaghi,et al. Deep Learning in Drug Target Interaction Prediction: Current and Future Perspective. , 2020, Current medicinal chemistry.
[3] Parvin Razzaghi,et al. DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks , 2020, Bioinform..
[4] Kayvan Najarian,et al. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper , 2020, Briefings Bioinform..
[5] Seongok Ryu,et al. Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation , 2019, J. Chem. Inf. Model..
[6] Xiangrong Liu,et al. deepDR: a network-based deep learning approach to in silico drug repositioning , 2019, Bioinform..
[7] Min Chen,et al. Revealing Drug-Target Interactions with Computational Models and Algorithms , 2019, Molecules.
[8] Hojung Nam,et al. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences , 2018, PLoS Comput. Biol..
[9] Meriem Bahi,et al. Deep semi-supervised learning for DTI prediction using large datasets and H2O-spark platform , 2018, 2018 International Conference on Intelligent Systems and Computer Vision (ISCV).
[10] Arzucan Özgür,et al. DeepDTA: deep drug–target binding affinity prediction , 2018, Bioinform..
[11] Vladimir B. Bajic,et al. DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches , 2017, Bioinform..
[12] Hyeon-Eui Kim,et al. Deep mining heterogeneous networks of biomedical linked data to predict novel drug‐target associations , 2017, Bioinform..
[13] Chee Keong Kwoh,et al. Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization , 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[14] Artem Cherkasov,et al. SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines , 2017, Journal of Cheminformatics.
[15] Ming Wen,et al. Deep-Learning-Based Drug-Target Interaction Prediction. , 2017, Journal of proteome research.
[16] G. Pazour,et al. Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness , 2017, Scientific Reports.
[17] Yanli Wang,et al. Predicting drug-target interactions by dual-network integrated logistic matrix factorization , 2017, Scientific Reports.
[18] Lei Xie,et al. Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem , 2016, Scientific Reports.
[19] M. Cecchini,et al. Ultrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease , 2016, Scientific Reports.
[20] Minoru Kanehisa,et al. KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..
[21] Thomas C. Wiegers,et al. The Comparative Toxicogenomics Database: update 2017 , 2016, Nucleic Acids Res..
[22] Keith C. C. Chan,et al. Large-scale prediction of drug-target interactions from deep representations , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[23] Yongdong Zhang,et al. Drug-target interaction prediction: databases, web servers and computational models , 2016, Briefings Bioinform..
[24] Chunyan Miao,et al. Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction , 2016, PLoS Comput. Biol..
[25] Ivan G. Costa,et al. A multiple kernel learning algorithm for drug-target interaction prediction , 2016, BMC Bioinformatics.
[26] Shuigeng Zhou,et al. Boosting compound-protein interaction prediction by deep learning , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[27] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[28] Scott Sanner,et al. AutoRec: Autoencoders Meet Collaborative Filtering , 2015, WWW.
[29] Hao Ding,et al. Similarity-based machine learning methods for predicting drug-target interactions: a brief review , 2014, Briefings Bioinform..
[30] Devansh Arpit,et al. Is Joint Training Better for Deep Auto-Encoders? , 2014 .
[31] Tapio Pahikkala,et al. Toward more realistic drug^target interaction predictions , 2014 .
[32] Tao Xu,et al. Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis , 2014, J. Chem. Inf. Model..
[33] Hao Ding,et al. Collaborative matrix factorization with multiple similarities for predicting drug-target interactions , 2013, KDD.
[34] S. Opella,et al. Structure determination of membrane proteins by nuclear magnetic resonance spectroscopy. , 2013, Annual review of analytical chemistry.
[35] Xing Chen,et al. Drug-target interaction prediction by random walk on the heterogeneous network. , 2012, Molecular bioSystems.
[36] V. Miranda,et al. Reconstructing Missing Data in State Estimation With Autoencoders , 2012, IEEE Transactions on Power Systems.
[37] Mindy I. Davis,et al. Comprehensive analysis of kinase inhibitor selectivity , 2011, Nature Biotechnology.
[38] John P. Overington,et al. ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..
[39] CHUN WEI YAP,et al. PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints , 2011, J. Comput. Chem..
[40] David S. Wishart,et al. DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..
[41] David Rogers,et al. Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..
[42] Jean-Philippe Vert,et al. Protein-ligand interaction prediction: an improved chemogenomics approach , 2008, Bioinform..
[43] Yoshihiro Yamanishi,et al. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.
[44] Christian von Mering,et al. STITCH: interaction networks of chemicals and proteins , 2007, Nucleic Acids Res..
[45] Robert B. Russell,et al. SuperTarget and Matador: resources for exploring drug-target relationships , 2007, Nucleic Acids Res..
[46] A. Barabasi,et al. Drug—target network , 2007, Nature Biotechnology.
[47] Geoffrey E. Hinton,et al. Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.
[48] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[49] J. Bajorath,et al. Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.
[50] Y.Z. Chen,et al. Ligand–protein inverse docking and its potential use in the computer search of protein targets of a small molecule , 2001, Proteins.
[51] J. Hendrickson. Similarity in Chemistry , 1991, Science.
[52] Vijay V. Raghavan,et al. A critical investigation of recall and precision as measures of retrieval system performance , 1989, TOIS.
[53] David Weininger,et al. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..
[54] Chee Keong Kwoh,et al. Drug-target interaction prediction by learning from local information and neighbors , 2013, Bioinform..
[55] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[56] Marvin Johnson,et al. Concepts and applications of molecular similarity , 1990 .