Classification of kinase inhibitors using a Bayesian model.

By Xiaoyang Xia, E. Maliski, P. Gallant, David Rogers. 2004

344 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

DeepTox: Toxicity Prediction using Deep Learning (2016, methodology): “Feature-based approaches include (generalized) linear models (e.g., Luco and Ferretti, 1997; Sagardia et al., 2013), random forests, (e.g., Svetnik et al., 2003; Polishchuk et al., 2009), and scoring schemes based on naive Bayes (Bender et al., 2004; Xia et al., 2004).”
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, methodology): “Naive Bayes learners and classifiers can be extremely fast compared to more sophisticated methods and have been widely used 102 .”
Toxicity Prediction using Deep Learning (2015, methodology): “Previous machine learning efforts were almost always ligand-based, such as scoring approaches like the Naive Bayes statistics (Xia et al., 2004; Nigsch et al., 2008; Mussa et al., 2013), density estimation (R. et al., 2012; Harper et al., 2001), nearest neighbor, support vector machines, and…”
Deep Learning as an Opportunity in Virtual Screening (2015, methodology): “Pipeline Pilot Bayesian Classiﬁers (PNPBC) The commercial product “Pipeline Pilot” [7] uses a Naive Bayes statistics based approach, which essentially contrasts the active samples of a target with the whole (background) compound database.”
Global mapping of pharmacological space (2006, neutral): “Using a Laplacian-modified Bayesian classifier approac”