Allehaibi Khalid, Daanial Khan Yaser, Khan Sher Afzal
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Department of Computer Science, University of Management and Technology, Lahore, Pakistan.
Appl Bionics Biomech. 2021 Sep 27;2021:2803147. doi: 10.1155/2021/2803147. eCollection 2021.
A crucial biological process called angiogenesis plays a vital role in migration, growth, and wound healing of endothelial cells and other processes that are controlled by chemical signals. Angiogenesis is the process that controls the growth of blood vessels within tissues while angiogenesis proteins play a significant role in the proper working of this process. The balancing of these signals is necessary for the proper working of angiogenesis. Unbalancing of these signals increases blood vessel formation, which causes abnormal growth or several diseases including cancer. The proposed work focuses on developing a two-layered prediction model using different classifiers like random forest (RF), neural network, and support vector machine. The first level performs in silico identification of angiogenesis proteins based on the primary structure. In the case the protein is an angiogenesis protein, then the second level predicts whether the protein is linked with tumor angiogenesis or not. The performance of the model is evaluated through various validation techniques. The model was evaluated using -fold cross-validation, independent, self-consistency, and jackknife testing. The overall accuracy using an RF classifier for angiogenesis at the first level was 97.8% and for tumor angiogenesis at the second level was 99.5%, ANN showed 94.1% accuracy for angiogenesis and 79.9% for tumor angiogenesis, and the accuracy of SVM for angiogenesis was 78.8% and for tumor angiogenesis was 65.19%.
一种名为血管生成的关键生物学过程在内皮细胞的迁移、生长和伤口愈合以及其他由化学信号控制的过程中起着至关重要的作用。血管生成是控制组织内血管生长的过程，而血管生成蛋白在这一过程的正常运作中发挥着重要作用。这些信号的平衡对于血管生成的正常运作是必要的。这些信号的失衡会增加血管形成，从而导致异常生长或包括癌症在内的多种疾病。所提出的工作重点是使用随机森林（RF）、神经网络和支持向量机等不同分类器开发一个两层预测模型。第一级基于一级结构对血管生成蛋白进行计算机识别。如果该蛋白质是血管生成蛋白，那么第二级预测该蛋白质是否与肿瘤血管生成有关。通过各种验证技术对模型的性能进行评估。该模型使用 - 折交叉验证、独立、自一致性和留一法测试进行评估。在第一级使用RF分类器对血管生成的总体准确率为97.8%，对肿瘤血管生成在第二级的准确率为99.5%，人工神经网络对血管生成的准确率为94.1%，对肿瘤血管生成的准确率为79.9%，支持向量机对血管生成的准确率为78.8%，对肿瘤血管生成的准确率为65.19%。
