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FTWSVM-SR: DNA-Binding Proteins Identification via Fuzzy Twin Support Vector Machines on Self-Representation

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Abstract

Due to the high cost of DNA-binding proteins (DBPs) detection, many machine learning algorithms (ML) have been utilized to large-scale process and detect DBPs. The previous methods took no count of the processing of noise samples. In this study, a fuzzy twin support vector machine (FTWSVM) is employed to detect DBPs. First, multiple types of protein sequence features are formed into kernel matrices; Then, multiple kernel learning (MKL) algorithm is utilized to linear combine multiple kernels; next, self-representation-based membership function is utilized to estimate membership value (weight) of each training sample; finally, we feed the integrated kernel matrix and membership values into the FTWSVM-SR model for training and testing. On comparison with other predictive models, FTWSVM based on SR (FTWSVM-SR) obtains the best performance of Matthew’s correlation coefficient (MCC): 0.7410 and 0.5909 on two independent testing sets (PDB186 and PDB2272 datasets), respectively. The results confirm that our method can be an effective DBPs detection tool. Before the biochemical experiment, our model can screen and analyze DBPs on a large scale.

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Acknowledgements

This study is supported by the National Science Foundation of China (NSFC 61873112, 61922020, 62172076 and 61902271) and Special Science Foundation of Quzhou (2021D004). The authors also thank professor Bin Liu, Xiuquan Du and Leyi Wei for kindly sharing the dataset.

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Author notes

  1. Yi Zou and Yijie Ding have contributed equally to this work.

Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Yi Zou & Li Peng

  2. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, People’s Republic of China

    Yijie Ding

  3. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, People’s Republic of China

    Quan Zou

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  1. Yi Zou
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Correspondence to Li Peng or Quan Zou.

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The related data can be download from: https://figshare.com/s/934f45e3a3e7693691d5.

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Zou, Y., Ding, Y., Peng, L. et al. FTWSVM-SR: DNA-Binding Proteins Identification via Fuzzy Twin Support Vector Machines on Self-Representation. Interdiscip Sci Comput Life Sci 14, 372–384 (2022). https://doi.org/10.1007/s12539-021-00489-6

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