臺灣國際科展

Predicting the Binding Affinity between Medicine and Estrogen Receptor Beta

科展類別
臺灣國際科展作品
屆次
2021年
科別
電腦科學與資訊工程
學校名稱
宜蘭縣私立慧燈高級中學
指導老師
林振慶;楊岱霖
作者
蔡孟學
關鍵字
Machine learning、Breast cancer、 Estrogen Receptor Beta

摘要或動機

Recent studies showed that the probability of Taiwanese females developing breast cancer has risen dramatically over the past 30 years. We are now facing younger and more breast cancer patients in Taiwan. What makes the matter even more severe, is the fact that patients that take cancer treating medicine will suffer from its serious side effects, some may even lose the ability to reproduce. We hope to develop a new system that can help doctors and researchers develop new medicine for treating breast cancer, the way medicine cures cancer tumors are by attaching onto the infected cells’ receptors. After collecting MACCS data (converted from SMILES), the dataset will be used for training the machine learning program. Due to the problem of insufficient training data, we used an ensemble method to generate our machine learning model. Among the three basic ensemble techniques, Max Voting, Averaging, and Weighted Averaging. we selected the max voting technique to perform the prediction for this research. We created two separate datasets, positive and negative, the two datasets will later be used as training data for the program. We weren’t sure of the ratio of positive and negative in the training data, therefore we compare 40 different ratios and evaluate the results. By comparing the accuracy of the models, we found out that when the ratio between positive data and negative data is 1:3000, the machine learning program will have the highest precision. After we created the final model through voting among the 1000 models generated, we evaluate the precision of the model through the following methods, AUC, precision, recall. The ultimate goal of this research is to assist doctors and researchers shorten the process of developing and testing new medicines.


「為配合國家發展委員會「推動ODF-CNS15251為政府為文件標準格式實施計畫」,以及 提供使用者有文書軟體選擇的權利,本館檔案下載部分文件將公布ODF開放文件格式, 免費開源軟體可至LibreOffice 下載安裝使用,或依貴慣用的軟體開啟文件。」

檔案名稱 檔案大小 格式
TISF2021-190006.pdf 1 MB Adobe Reader(Pdf)檔案