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Evaluation of Mutual Information and Genetic Programming for Feature Selection in QSAR

https://doi.org/10.1021/ci049933v
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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N-Version Genetic Programming via Fault Masking
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GA strategy for variable selection in QSAR studies: GAPLS and D-optimal designs for predictive QSAR model
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Neural networks and genetic algorithms in drug design
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Genetic Programming: A Novel Method for the Quantitative Analysis of Pyrolysis Mass Spectral Data
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Active Learning with Support Vector Machines in the Drug Discovery Process
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Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k -Nearest-Neighbor Principle
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GA Strategy for Variable Selection in QSAR Studies: Application of GA-Based Region Selection to a 3D-QSAR Study of Acetylcholinesterase Inhibitors
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Chance factors in studies of quantitative structure-activity relationships
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A preliminary structure-activity study of the mixed-function oxidase inhibitor, 7,8-benzoflavone
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Multiobjective Optimization in Quantitative Structure−Activity Relationships: Deriving Accurate and Interpretable QSARs
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Robust QSAR Models Using Bayesian Regularized Neural Networks
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Neural Networks for Pattern Recognition
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Information retrieval
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Information Theory and Statistics.
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Genetic algorithms in search, optimization, and machine learning
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The effect of class distribution on classifier learning: an empirical study

What points at this work

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Additional file 2 of Profiling and analysis of chemical compounds using pointwise mutual information
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