Bioinformatics and Computational Omics. Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University (TMU), Tehran, Iran.
Computer Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
J Transl Med. 2019 Mar 4;17(1):71. doi: 10.1186/s12967-019-1813-7.
Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment.
A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study. Also, 20% of each class were selected to construct an independent test dataset (see Additional files 1, 2). We proposed an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17,000 features were extracted to encode the peptides.
Finally, more than 2000 informative features were selected to train the classifiers for detecting anti-angiogenic peptides. AntAngioCOOL includes three different models that can be selected by the user for different purposes; it is the most sensitive, most specific and most accurate. According to the obtained results AntAngioCOOL can effectively suggest anti-angiogenic peptides; this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on the independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/ .
Only 2% of the extracted descriptors were used to build the predictor models. The results revealed that physico-chemical profile is the most important feature type in predicting anti-angiogenic peptides. Also, atomic profile and PseAAC are the other important features.
血管生成抑制研究是血管生成依赖性疾病治疗的一个前沿领域，尤其是在癌症治疗方面。最近，抗血管生成肽的研究在癌症治疗领域取得了有前景的成果。
本研究使用了一个非冗余的 135 个抗血管生成肽（阳性实例）和 135 个非抗血管生成肽（阴性实例）数据集。此外，每类中选择 20%的样本用于构建独立的测试数据集（见附加文件 1、2）。我们提出了一种有效的基于机器学习的 R 包（AntAngioCOOL）来预测抗血管生成肽。我们已经检验了 200 多种不同的分类器来构建高效的预测器。此外，还提取了 17000 多个特征来编码肽。
最后，选择了 2000 多个信息丰富的特征来训练分类器以检测抗血管生成肽。AntAngioCOOL 包括三个不同的模型，用户可以根据不同的目的选择；它是最敏感、最特异和最准确的。根据获得的结果，AntAngioCOOL 可以有效地提示抗血管生成肽；该工具在独立测试集上的灵敏度为 88%，特异性为 77%，准确性为 75%。AntAngioCOOL 可在 https://cran.r-project.org/ 访问。
仅使用提取的描述符的 2%构建预测器模型。结果表明，物理化学特征是预测抗血管生成肽的最重要的特征类型。此外，原子特征和 PseAAC 也是重要的特征。
