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Serial fuzzy system algorithm for predicting biological activity of anti-breast cancer compounds

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Abstract

Breast cancer has become one of the most common and deadly cancers in the world, and its treatment has been the focus of research. In the search for breast cancer drug candidate compounds, it is important to establish an effective quantitative structure-activity relationship for drug research and development. Neural networks have achieved high accuracy in this field, but with shortcomings of a large number of parameters, high model complexity, and poor interpretability. Therefore, a Serial Fuzzy System built by Subtractive clustering and ANFIS (SFSSA) layer by layer is proposed to explore a solution with better interpretability. Through the experiment in the bioactivity data set of candidate compounds with several models, the following conclusions are found: 1) The precision of SFSSA is better than that of classic linear regression; 2) SFSSA has fewer parameters and rules, and has better interpretability and generalization ability than classic neural network algorithms; 3) SFSSA has less training time and higher prediction accuracy than optimized TSK fuzzy system algorithm MBGD-RDA (Minibatch Gradient Descent with Regularization, DropRule, and AdaBound); 4) SFSSA’s subsystem with 15 inputs achieved best prediction effect. In short, SFSSA provides a new way to apply fuzzy systems for high-dimensional regression problems.

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Acknowledgments

The main idea of this paper dates back to 10 years ago, when Dewang Chen, the corresponding author of this paper, was visiting University of California at Berkeley as a visiting scholar of Prof. Lotfi Zadeh, father of fuzzy logic and member of Academy of Engineering of USA. In the discussion during the serial seminars named as “Interpretability vs Accuracy” hosted by Prof. Zadeh, the idea of deep fuzzy modeling occurred to Chen’ mind. After long-time thinking, coding and writing, this article was formed. We would like to express our gratitude to the late Prof. Zadeh, who inspired us to pursue interpretable AI, not just high-accuracy AI.

This work is jointly supported by the National Natural Science Foundation of China under Grant 61976055, the special fund for education and scientific research of Fujian Provincial Department of Finance under Grant GY-Z21001, and open project support of State Key Laboratory of Management and Control for Complex Systems under Grant 20210116.

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Authors and Affiliations

  1. School of Transportation, Fujian University of Technology, Fuzhou, 350118, China

    Wendi Zhao, Dewang Chen & Xiaoyu Zheng

  2. School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, China

    Yuqi Lu

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Correspondence to Dewang Chen.

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Zhao, W., Chen, D., Zheng, X. et al. Serial fuzzy system algorithm for predicting biological activity of anti-breast cancer compounds. Appl Intell 53, 13801–13814 (2023). https://doi.org/10.1007/s10489-022-04134-7

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