Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization
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Chee Keong Kwoh | Xiaoli Li | Ali Ezzat | Peilin Zhao | Min Wu | P. Zhao | Xiaoli Li | C. Kwoh | Min Wu | Ali Ezzat
[1] John Riedl,et al. Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .
[2] Amy Nicole Langville,et al. Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization , 2014, ArXiv.
[3] Yoshihiro Yamanishi,et al. Supervised prediction of drug–target interactions using bipartite local models , 2009, Bioinform..
[4] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[5] Susumu Goto,et al. SIMCOMP/SUBCOMP: chemical structure search servers for network analyses , 2010, Nucleic Acids Res..
[6] M S Waterman,et al. Identification of common molecular subsequences. , 1981, Journal of molecular biology.
[7] Tapio Pahikkala,et al. Toward more realistic drug^target interaction predictions , 2014 .
[8] E. Marchiori,et al. Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile , 2013, PloS one.
[9] Hao Ding,et al. Collaborative matrix factorization with multiple similarities for predicting drug-target interactions , 2013, KDD.
[10] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[11] Natalia Novac,et al. Challenges and opportunities of drug repositioning. , 2013, Trends in pharmacological sciences.
[12] Elena Marchiori,et al. Gaussian interaction profile kernels for predicting drug-target interaction , 2011, Bioinform..
[13] Philip E. Bourne,et al. Predicting the Polypharmacology of Drugs: Identifying New Uses through Chemoinformatics, Structural Informatics, and Molecular Modeling‐Based Approaches , 2012 .
[14] P. Sanseau,et al. Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.
[15] Michael J. Keiser,et al. Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.
[16] Daniel R. Caffrey,et al. Structure-based maximal affinity model predicts small-molecule druggability , 2007, Nature Biotechnology.
[17] Mehmet Gönen,et al. Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization , 2012, Bioinform..
[18] Xiaojun Wu,et al. Graph Regularized Nonnegative Matrix Factorization for Data Representation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[20] David S. Wishart,et al. DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..
[21] Ali Masoudi-Nejad,et al. Drug–target interaction prediction via chemogenomic space: learning-based methods , 2014, Expert opinion on drug metabolism & toxicology.
[22] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[23] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[24] Ethem Alpaydin,et al. Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..
[25] Chris H. Q. Ding,et al. Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs , 2010, SDM.
[26] Susumu Goto,et al. KEGG for integration and interpretation of large-scale molecular data sets , 2011, Nucleic Acids Res..
[27] Chee Keong Kwoh,et al. Drug-target interaction prediction by learning from local information and neighbors , 2013, Bioinform..
[28] John P. Overington,et al. ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..
[29] Yoshihiro Yamanishi,et al. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.
[30] Damian Szklarczyk,et al. STITCH 4: integration of protein–chemical interactions with user data , 2013, Nucleic Acids Res..
[31] Anne Mai Wassermann,et al. Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects , 2009, J. Chem. Inf. Model..