Abstract：Medical research results show that the effectiveness of an antitumor drug is highly dependent on the genomic characteristics of patients. How to customize an optimal medical treatment for each tumor patient is an extremely important and challenging research topic. Aiming at this subject, a set of methods to predict the efficacy response of various antitumor drugs is proposed by machine learning technology for data processing, feature extraction and modeling of the tumor gene sequences of patients. Firstly, a data mining algorithm based on multiscale association rules is proposed for feature selection at different scales of genomics data. Then, the selected genomics data are locally accumulated by the cumulative window function to further compress the data and extract the gene feature information with stronger overall expression. Based on the above, a fully connected multi-layer neural network is designed and the extracted multiscale cumulative gene features are treated as input samples to train the network. Finally, two fusion methods, including feature fusion and decision fusion, are utilized to predict the responses of a tumor gene sequence to different antitumor drugs, respectively. Results of simulation experiments show that the proposed approach is superior in key performance indexes, such as sensitivity, specificity, accuracy and the area under characteristic curve.
韩睿, 郭成安. 基于多尺度局部累积特征和神经网络的抗肿瘤药物反应预测[J]. 模式识别与人工智能, 2022, 35(4): 323-332.
HAN Rui, GUO Cheng'an. Prediction of Antitumor Drug Response Based on Multiscale Local Cumulative Features and Neural Networks. Pattern Recognition and Artificial Intelligence, 2022, 35(4): 323-332.
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