Department of Bioinformatics, Indian Institute of Information Technology Allahabad Deoghat, Jhalwa, Allahabad, 211012, Uttar Pradesh, India.
Interdiscip Sci. 2011 Sep;3(3):232-9. doi: 10.1007/s12539-011-0102-9. Epub 2011 Jun 14.
Mutagenicity is the capability of a chemical to carry out mutations in genetic material of an organism. In order to curtail expensive drug failures due to mutagenicity found in late development or even in clinical trials, it is crucial to determine potential mutagenicity problems as early as possible. In this work we have proposed three different classifiers, i.e. Support Vector Machine (SVM), Artificial Neural Network (ANN) and bayesian classifiers, for the prediction of mutagenicity of compounds based on seventeen descriptors. Among the three classifiers Radial Basis Function (RBF) kernel based SVM classifier appeared to be more accurate for classifying the compounds under study on mutagens and non-mutagens. The overall prediction accuracy of SVM model was found to be 71.73% which was appreciably higher than the accuracy of ANN based classifier (59.72%) and bayesian classifier (66.61%). It suggests that SVM based prediction model can be used for predicting mutagenicity more accurately compared to ANN and bayesian classifier for data under consideration.
致突变性是一种化学物质使生物体遗传物质发生突变的能力。为了减少因在后期开发甚至临床试验中发现致突变性而导致昂贵药物失败的情况，尽早确定潜在的致突变性问题至关重要。在这项工作中，我们提出了三种不同的分类器，即支持向量机（SVM）、人工神经网络（ANN）和贝叶斯分类器，用于根据十七个描述符预测化合物的致突变性。在这三种分类器中，基于径向基函数（RBF）核的 SVM 分类器似乎更能准确地区分研究中的致突变化合物和非致突变化合物。SVM 模型的整体预测准确性为 71.73%，明显高于基于 ANN 的分类器（59.72%）和贝叶斯分类器（66.61%）的准确性。这表明，与 ANN 和贝叶斯分类器相比，SVM 基于预测模型可更准确地预测致突变性，尤其是针对所考虑的数据。
