Gaussian interaction profile kernels for predicting drug-target interaction

By T. van Laarhoven, Sander B. Nabuurs, Elena Marchiori. Bioinform., 2011

844 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7934fd41b3749a6d3b0edbadffaba4f12cbbd73b

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In Computational Theory and Mathematics

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Citations in the corpus

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “…the other projection of drug–target networks, nodes are the drug targets, which are connected, if they both bind the same drug (Keiser et al., 2007, 2009; Ma'ayan et al., 2007; Yildirim et al., 2007; Hert et al., 2008; Yamanishi et al., 2008; Bleakley & Yamanishi, 2009; van Laarhoven et al., 2011).”
Deep learning with feature embedding for compound-protein interaction prediction (2016, methodology): “Numerous computational methods have been proposed to predict potential compound-protein interactions in silico to narrow the large search space of possible interacting compound-protein pairs and facilitate the drug discovery and development process [7] [8] [9] [10] [11] [12] [13] [14].”
SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017, methodology): “In similarity-based methods [3, 27–32], similarity matrices for both the drug–drug pairs and the target–target pairs are generated.”
Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization (2012, methodology): “Received on May 27, 2012; revised on May 27, 2012; accepted on June 18, 2012”
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013, background): “Keywords Chemoinformatics; Drug-target interaction; Weighted low-rank approximation; Multiple types of similarities over drugs and targets”
Designing antimicrobial peptides: form follows function (2011, methodology): “Socalled kernel methods have recently been added to the set of algorithms that seem to be par ticularly suited for the purpose of identifying drug–target interaction pairs 48 .”
Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences (2018, methodology): “fiers with GIP kernels van Laarhoven et al. (2011), and KBMF2K-”
Drug-target interaction prediction by learning from local information and neighbors (2013, methodology): “To facilitate comparison with published approaches, we used the same groups of four datasets which are first analyzed by Yamanishi et al. (2008) and then later by Bleakley and Yamanishi (2009), Xia et al.”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “This have been previously proposed by authors in [5, 131, 153, 302, 303].”
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “Following similar settings in previous studies [12, 14, 15, 19, 24], four well-known biological databases, i.”
Toward more realistic drug–target interaction predictions (2014, positive): “The LDO and LTO concepts are analogous to the LOO-CV approach, which have been widely used in the previous works under setting S1 [17].”