Machine Learning Prediction for 50 Anti-Cancer Food Molecules from 968 Anti-Cancer Drugs
Machine Learning Prediction for 50 Anti-Cancer Food Molecules from 968 Anti-Cancer Drugs
复制标题
机器学习预测 968 种抗癌药物中的 50 种抗癌食物分子
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Peixuan Xu
中科院分区：
文献类型：
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作者：
Simiao Zhao;Xuan Mao;Hanghong Lin;H. Yin;Peixuan Xu
许多类型的食物和潜在的抗癌治疗药物中都富含抗癌分子（CBM）。在之前的工作中，研究人员引入了基于网络的机器学习平台来识别抗癌分子，例如比较已批准的抗癌药物和食物分子之间分子网络的相似性。在这里，我们的目标是在这项工作的基础上提高预测食物分子的准确性。在这个项目中，我们通过将软投票算法应用于七种机器学习算法来改进监督学习方法：具有径向基函数的支持向量机（具有 RBF 内核的 SVM）、多层感知器神经网络 (MLP)、随机森林、决策树、高斯朴素贝叶斯、Adaboosting 和 Bagging。结果，所使用的 50 个食物分子的数据集的准确性从 82% 提高到 87%，实现了预测抗癌分子的精确度的显着提高。
Cancer-beating molecules (CBMs) are abundant in many types of food and potentially anti-cancer therapeutic agents. In the previous work, researchers introduced a network-based machine learning platform to identify the cancer-beating molecules, for example, comparing the similarities in the molecular network between approved anticancer drug and food molecules. Herein, we aim to build on this work to enhance the accuracy of predicting food molecules. In this project, we improve supervised learning approaches by applying Soft Voting algorithm to seven machine learning algorithms: Support Vector Machine with Radial Basis Function (SVM with RBF kernel), multilayer perceptron neural network (MLP), Random forest, Decision trees, Gaussian Naive Bayes, Adaboosting, and Bagging. As a result, the accuracy in the dataset of 50 food molecules utilized increased from 82% to 87%, achieving a significant improvement in the precision of predicting anti-cancer molecules.