Frontiers in Medical Science Research, 2022, 4(7); doi: 10.25236/FMSR.2022.040708.
Songhao Lu, Xinxin Fu, Mingzhou Chen, Xuezheng Yue
School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai, 200093, China
Breast cancer poses a serious threat to global women's health. Around this problem, a quantitative prediction model of biological activity based on neural network and a quantitative prediction model of multiple linear regression are established in this paper. 20 kinds of molecular descriptor data with the most significant effect on biological activity of 1974 compounds were selected as input layer and 1 biological activity data as output layer, and a quantitative prediction model of biological activity based on neural network was established. Based on the quantitative prediction model based on parameter setting, the single hidden layer BP neural network structure is used to test the model, and the fitting error R values of the training set and test set of the model are close to 1, and the trend in the scatter diagram is obvious, which shows that the prediction value of the model is more accurate.
Cancer, BP neural network, Model checking
Songhao Lu, Xinxin Fu, Mingzhou Chen, Xuezheng Yue. Study on Quantitative Prediction of Anticancer Compound Activity Based on Neural Network Model. Frontiers in Medical Science Research (2022) Vol. 4, Issue 7: 48-51. https://doi.org/10.25236/FMSR.2022.040708
[1] Liu Zuwang, Wang Yumei Meta analysis of dietary fiber and carbohydrate intake associated with breast cancer risk [J]. China health statistics, (03): 464-4672015.
[2] Zhang Cuifeng, Xie Haitang, pan Guoyu Absorption, distribution, metabolism, excretion and toxicity characteristics of macromolecular drugs and application of pharmacokinetic model [J]. Journal of pharmacy, (8): 1202-1208, 2016.
[3] Burden F R. A chemically intuitive molecular index based on the eigenvalues of a modified adjacency matrix[J]. Quantitative Structure‐Activity Relationships, 16(4): 309-314, 1997.
[4] Lin Wenfu. Econometrics [M]. Shanghai University of Finance and Economics Press, 2005.
[5] Rumelhart D E, Hinton G E, Williams R J. Learning Internal Representation by BackPropagation Errors [J]. Nature, 323: 533-536, 1986.
[6] Li Jie. Improved particle swarm optimization optimization support vector machine for project cost prediction [J]. Computer system application, 25 (006): 202-2062016.