Biomedical Knowledge Engineering, Seoul National University, Seoul, Republic of Korea.
School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Republic of Korea.
BMC Bioinformatics. 2022 May 5;23(1):163. doi: 10.1186/s12859-022-04698-8.
To reduce drug side effects and enhance their therapeutic effect compared with single drugs, drug combination research, combining two or more drugs, is highly important. Conducting in-vivo and in-vitro experiments on a vast number of drug combinations incurs astronomical time and cost. To reduce the number of combinations, researchers classify whether drug combinations are synergistic through in-silico methods. Since unstructured data, such as biomedical documents, include experimental types, methods, and results, it can be beneficial extracting features from documents to predict anti-cancer drug combination synergy. However, few studies predict anti-cancer drug combination synergy using document-extracted features.
We present a novel approach for anti-cancer drug combination synergy prediction using document-based feature extraction. Our approach is divided into two steps. First, we extracted documents containing validated anti-cancer drug combinations and cell lines. Drug and cell line synonyms in the extracted documents were converted into representative words, and the documents were preprocessed by tokenization, lemmatization, and stopword removal. Second, the drug and cell line features were extracted from the preprocessed documents, and training data were constructed by feature concatenation. A prediction model based on deep and machine learning was created using the training data. The use of our features yielded higher results compared to the majority of published studies.
Using our prediction model, researchers can save time and cost on new anti-cancer drug combination discoveries. Additionally, since our feature extraction method does not require structuring of unstructured data, new data can be immediately applied without any data scalability issues.
为了降低药物的副作用并提高其治疗效果，与单一药物相比，药物联合研究结合两种或多种药物非常重要。对大量药物组合进行体内和体外实验需要耗费大量的时间和成本。为了减少组合数量，研究人员通过计算机方法对药物组合是否协同作用进行分类。由于生物医学文献等非结构化数据包含实验类型、方法和结果，因此从文献中提取特征来预测抗癌药物组合协同作用可能会有所帮助。然而，很少有研究使用基于文档提取的特征来预测抗癌药物组合协同作用。
我们提出了一种基于文档特征提取的抗癌药物组合协同作用预测新方法。我们的方法分为两步。首先，我们提取了包含已验证的抗癌药物组合和细胞系的文献。提取文献中的药物和细胞系同义词被转换为代表词，并且对文档进行了分词、词干提取和停用词去除的预处理。其次，从预处理的文档中提取药物和细胞系特征，并通过特征串联构建训练数据。使用训练数据创建了基于深度学习和机器学习的预测模型。与大多数已发表的研究相比，我们的特征的使用产生了更高的结果。
使用我们的预测模型，研究人员可以节省新抗癌药物组合发现的时间和成本。此外，由于我们的特征提取方法不需要对非结构化数据进行结构化，因此可以立即应用新数据，而不会出现任何数据可扩展性问题。
