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Logical analysis of data in structure-activity investigation of polymeric gene delivery

机译：聚合基因传递的结构活性研究中数据的逻辑分析

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摘要

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. For the first time semiempirical models for prediction of DNA transfection by poly(β-amino esters), a promising class of biodegradable polymeric carriers, are built using recently introduced extension of logical analysis of data (LAD) methodology, in particular, the LAD regression technique. The employed algorithm demonstrates higher predictive accuracy than that of conventional regression techniques developed on the neural net platforms.
机译：迄今为止，半经验或替代模型已经证明在预测聚合物材料的生物学相关特性方面取得了巨大的成功。首次使用新颖的数据逻辑分析技术（LAD）建立了聚（β-氨基酯）的化学结构与其转染DNA效率之间的相关性。介绍了线性组合和显式表示模型，并在本研究的框架内进行了比较。最成功的回归模型在转染效率的预测值和实验测量值之间产生了令人满意的一致性（Pearson相关系数为0.77;平均绝对误差为3.83）。结果表明，对LAD算法提供的规则进行的详细分析为高分子化学家设计新的生物材料提供了实用的工具。首次使用最近引入的数据逻辑分析（LAD）方法扩展，特别是LAD回归，建立了预测聚（β-氨基酯）进行DNA转染的半经验模型，这是一种有前途的可生物降解的聚合物载体。技术。与在神经网络平台上开发的传统回归技术相比，所采用的算法显示出更高的预测准确性。

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