Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization

By Mehmet Gönen. Bioinform., 2012

373 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5958275b3b05802cee90c589a252b7a0c9c70dd9

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In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

A Survey on Distributed Machine Learning (2019, background): “Similarly factorizing a Drug compound-Target Protein Matrix is used for new drug discovery [63].”
Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences (2018, methodology): “…methods specifically for CPI prediction, i.e. BLM Bleakley and Yamanishi (2009), the RLS-avg and RLS-Kron classifiers with GIP kernels van Laarhoven et al. (2011), and KBMF2Kclassification and KBMF2K-regression Gönen (2012), which were run using the same experimental settings as Liu et al. (2015).”
Toward more realistic drug–target interaction predictions (2014, neutral): “…prediction studies seem to have ignored these lessons when introducing new and improved prediction models, although there are few exceptions [8, 41] For instance, many recent works have investigated the effect of including other types of pharmacological information into the drug–target…”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “A method for factorizing the interaction score matrix in terms of kernel matrices (similarity matrices), which can be used as DTI predictors for new drugs and protein KBMF2K [194]. inference methods and uses only DT bipartite network topology similarity [195].”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, methodology): “For GIP and KBMF2K, we used the software provided by the authors, which were run by the same parameter settings as those in [30] and [31], respectively.”
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions (2013, methodology): “These datasets were collected from four general databases and frequently used in predicting drug-target interactions [2, 28, 26, 9].”
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “Therefore, NRLMF is more efficient than KBMF2K and CMF.”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “tionships between a drug and proteins that the drug might target [48], i.”
Review of Drug Repositioning Approaches and Resources (2018, methodology): “For instance, some researchers grouped drug repositioning methods according to the biological networks used [19], and others divided drug repositioning methods into two types: data-driven and hypothesis-driven [21] .”