Data strip mining for the virtual design of pharmaceuticals with neural networks

By R. Kewley, Mark J. Embrechts, Curt M. Breneman. 2000

152 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry

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Citations in the corpus

Modeling wine preferences by data mining from physicochemical properties (2009, background): “Received 28 July 2008 Received in revised form 22 May 2009 Accepted 28 May 2009 Available online 6 June 2009”
Data mining in soft computing framework: a survey (2002, background): “Some recent work in this direction are available in Ref. [83], [ 84 ], [85], [86].”
Mortality assessment in intensive care units via adverse events using artificial neural networks (2006, methodology): “A better alternative is to use sensitivity analysis [26], which has outperformed other input selection techniques, such as forward selection and genetic algorithms.”
Identification and Review of Sensitivity Analysis Methods (2002, methodology): “Sensitivity analysis methods have been applied in various fields, including complex engineering systems, economics, physics, social sciences, medical decision making, and others (e.g., Oh and Yang, 2000; Baniotopoulos, 1991; Helton and Breeding, 1993; Cheng, 1991; Beck et al., 1997; Agro et al., 199”
Data Mining for Molecules with 2-D Neural Network Sensitivity Analysis (2003, methodology): “Kewley et al. (1998; 2000) have proposed a novel ANN-based feature selection technique for QSAR called Data Strip Mining (DSM).”
Using sensitivity analysis and visualization techniques to open black box data mining models (2013, methodology): “The extraction of rules is the most popular solution [29][26][23].”
A data mining approach to predict forest fires using meteorological data (2007, methodology): “Then, a sensitivity analysis [16] procedure was performed by measuring the variance ( V a ) produced by the output when a given input attribute x a varies through its entire range with L levels (here set to L = 5 ).”
Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool (2010, methodology): “We use the variance (Va) of ya,j as a measure of input relevance [9].”
Using Data Mining for Wine Quality Assessment (2009, methodology): “The difference, when compared to the standard backward selection, is that we guide the variable deletion (at each step) by the sensitivity analysis, in a variant that allows a reduction of the computational effort by a factor of I and that in [14] has outperformed other methods (e.”