Open AccessProceedings Article
Data Analysis using G/SPLINES
David Rogers
- 02 Dec 1991
- Vol. 4, pp 1088-1095
TL;DR: G/SPLINES is an algorithm for building functional models of data which produces a population of models which evolve over time rather than a single model, which allows analysis not possible with other regression-based approaches.
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Abstract: G/SPLINES is an algorithm for building functional models of data. It uses genetic search to discover combinations of basis functions which are then used to build a least-squares regression model. Because it produces a population of models which evolve over time rather than a single model, it allows analysis not possible with other regression-based approaches.
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Citations
Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships
David Rogers,Anton J. Hopfinger +1 more
TL;DR: The genetic function approximation (GFA) algorithm is applied to three published data sets to demonstrate it is an effective tool for doing both QSAR and QSPR.
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Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR).
Pathan Mohsin Khan,Kunal Roy +1 more
TL;DR: This review provides an overview of various feature selection methods as well as different statistical learning algorithms for QSAR modeling at an elementary level for nonexpert readers.
91
Chemometric modeling of aquatic toxicity of contaminants of emerging concern (CECs) in Dugesia japonica and its interspecies correlation with daphnia and fish: QSTR and QSTTR approaches.
Kazi Amirul Hossain,Kunal Roy +1 more
TL;DR: In this paper, the authors developed quantitative structure-toxicity relationship (QSTR) models using a data set of 75 compounds for the prediction of aquatic ecotoxicity of CECs on fresh water planarian (Dugesia japonica) by partial least squares (PLS) regression algorithm using simple molecular descriptors selected by genetic algorithm approach.
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Application of Genetic Algorithms to the General QSAR Problem and to Guiding Molecular Diversity Experiments
A.J. Hopfinger,H.C. Patel +1 more
- 01 Jan 1996
TL;DR: This chapter explores how GFA can be used to establish reliable quantitative structure–activity relationships (QSARs) when multiple conformations, shape references, and observed biological activities are given for each compound in the training database.
13
QSPR Models for the Prediction of Friction Coefficient and Maximum Non-Seizure Load of Lubricants
TL;DR: In this article, a quantitative structure-property relationship (QSPR) model was used to study the friction coefficient and maximum non-seizure load of fatty acids, alcohols, and esters as extreme pressure and antiwear additives in lubricants on the surface of steel using several physicochemical descriptors.
11
References
Multivariate Adaptive Regression Splines
TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm
David Rogers
- 01 May 1991
TL;DR: G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines algorithm with Holland's Genetic Algorithm, where the incremental search is replaced by a genetic search.