使用分子描述符预测聚酰胺-胺树枝状大分子的细胞毒性。
Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors.
作者信息
Jones David E, Ghandehari Hamidreza, Facelli Julio C
机构信息
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA.
Department of Bioengineering, University of Utah, Salt Lake City, UT 84112, USA ; Department of Pharmaceutics and Pharmaceutical Chemistry, University of Utah, Salt Lake City, UT 84112, USA, ; Utah Center for Nanomedicine, Nano Institute of Utah, University of Utah, Salt Lake City, UT 84112, USA.
出版信息
Beilstein J Nanotechnol. 2015 Sep 11;6:1886-96. doi: 10.3762/bjnano.6.192. eCollection 2015.
The use of data mining techniques in the field of nanomedicine has been very limited. In this paper we demonstrate that data mining techniques can be used for the development of predictive models of the cytotoxicity of poly(amido amine) (PAMAM) dendrimers using their chemical and structural properties. We present predictive models developed using 103 PAMAM dendrimer cytotoxicity values that were extracted from twelve cancer nanomedicine journal articles. The results indicate that data mining and machine learning can be effectively used to predict the cytotoxicity of PAMAM dendrimers on Caco-2 cells.
数据挖掘技术在纳米医学领域的应用一直非常有限。在本文中，我们证明了数据挖掘技术可用于利用聚（酰胺胺）（PAMAM）树枝状大分子的化学和结构性质来开发其细胞毒性预测模型。我们展示了使用从十二篇癌症纳米医学期刊文章中提取的103个PAMAM树枝状大分子细胞毒性值开发的预测模型。结果表明，数据挖掘和机器学习可有效地用于预测PAMAM树枝状大分子对Caco-2细胞的细胞毒性。
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