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Logical Analysis of Data in Structure‐Activity Investigation of Polymeric Gene Delivery
Author(s) -
Gubskaya Anna V.,
Bonates Tiberius O.,
Kholodovych Vladyslav,
Hammer Peter,
Welsh William J.,
Langer Robert,
Kohn Joachim
Publication year - 2011
Publication title -
macromolecular theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.37
H-Index - 56
eISSN - 1521-3919
pISSN - 1022-1344
DOI - 10.1002/mats.201000087
Subject(s) - pearson product moment correlation coefficient , correlation coefficient , representation (politics) , polymer , biological system , linear regression , computer science , regression analysis , chemistry , materials science , mathematics , statistics , organic chemistry , machine learning , politics , political science , law , biology
To date semi‐empirical or surrogate modeling has demonstrated great success in the prediction of the biologically relevant properties of polymeric materials. For the first time, a correlation between the chemical structures of poly(β‐amino esters) and their efficiency in transfecting DNA was established using the novel technique of logical analysis of data (LAD). Linear combination and explicit representation models were introduced and compared in the framework of the present study. The most successful regression model yielded satisfactory agreement between the predicted and experimentally measured values of transfection efficiency (Pearson correlation coefficient, 0.77; mean absolute error, 3.83). It was shown that detailed analysis of the rules provided by the LAD algorithm offered practical utility to a polymer chemist in the design of new biomaterials.