Performance of multicomponent self-organizing regression (MCSOR) in QSAR,QSPR, and multivariate calibration: comparison with partial least-squares (PLS) and validation with large external data sets |
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Authors: | K Tuppurainen S-P Korhonen J Ruuskanen |
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Institution: | 1. Department of Chemistry , University of Kuopio , PO Box 1627, Kuopio, Finland Kari.Tuppurainen@uku.fi;3. Department of Chemistry , University of Kuopio , PO Box 1627, Kuopio, Finland;4. Department of Environmental Sciences , University of Kuopio , Finland |
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Abstract: | A novel method for underdetermined regression problems, multicomponent self-organizing regression (MCSOR), has been recently introduced. Here, its performance is compared with partial least-squares (PLS), which is perhaps the most widely adopted multivariate method in chemometrics. A potpourri of models is presented, and MCSOR appears to provide highly predictive models that are comparable with or better than the corresponding PLS models in large internal (leave-one-out, LOO) and pseudo-external (leave-many-out, LMO) validation tests. The “blind” external predictive ability of MCSOR and PLS is demonstrated employing large melting point, factor Xa, log?P and log?S data sets. In a nutshell, MCSOR is fast, conceptually simple (employing multiple linear regression, MLR, as a statistical tool), and applicable to all kinds of multivariate problems with single Y-variable. |
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Keywords: | MCSOR PLS QSAR QSPR Multivariate calibration |
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