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String kernels construction and fusion: a survey with bioinformatics application

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Abstract

The kernel method, especially the kernel-fusion method, is widely used in social networks, computer vision, bioinformatics, and other applications. It deals effectively with nonlinear classification problems, which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation, enabling the use of kernel methods to predict the structure and function of sequences. Therefore, the kernel method is significant in the solution of bioinformatics problems. Various kernels applied in bioinformatics are explained clearly, which can help readers to select proper kernels to distinguish tasks. Mass biological sequence data occur in practical applications. Research of the use of machine learning methods to obtain knowledge, and how to explore the structure and function of biological methods for theoretical prediction, have always been emphasized in bioinformatics. The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction. This review focuses on the requirements of classification tasks of biological sequence data. It studies kernel methods and optimization algorithms, including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (Grant Nos. 61922020, 61771331, 61902259).

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Ren Qi & Fei Guo

  2. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610056, China

    Quan Zou

  3. Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, 571158, China

    Quan Zou

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  1. Ren Qi
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  2. Fei Guo
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  3. Quan Zou
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Corresponding author

Correspondence to Quan Zou.

Additional information

Ren Qi received the MS degrees in computer technology from the College of Intelligence and Computing, Tianjin University, China in 2018 and she is a doctoral student in College of Intelligence and Computing from Tianjin University, China in 2018.

Her research interests include machine learning, metric learning, and bioinformatics. Several related works have been published by IJCAI, Journal of software.

Fei Guo received the BSc and the PhD degrees in School of Computer Science and Technology, Shandong University, China in 2007 and 2012, respectively. She is currently an assistant professor in the College of Intelligence and Computing, Tianjin University, China.

Her research interests include bioinformatics, algorithms, machine learning. Several related works have been published by Briefings in Bioinformatics, Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, etc. She is the PC member of APBC-2016, APBC-2017, and reviewer of BMC Bioinformatics, BMC System Biology, IEEE TCBB, PLoS ONE, etc.

Quan Zou (M’13-SM’17) received the BSc, MSc and the PhD degrees in computer science from Harbin Institute of Technology, China in 2004, 2007 and 2009, respectively. He worked in Xiamen University and Tianjin University, China, from 2009 to 2018 as an assistant professor, associate professor and professor. He is currently a professor in the Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, China.

His research is in the areas of bioinformatics, machine learning and parallel computing. Several related works have been published by Science, Briefings in Bioinformatics, Bioinformatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, etc. Google scholar showed that his more than 100 papers have been cited more than 5000 times. He is the editor-in-chief of Current Bioinformatics, associate editor of IEEE Access, and the editor board member of Computers in Biology and Medicine, Genes, Scientific Reports, etc. He was selected as one of the Clarivate Analytics Highly Cited Researchers in 2018 and 2021.

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Qi, R., Guo, F. & Zou, Q. String kernels construction and fusion: a survey with bioinformatics application. Front. Comput. Sci. 16, 166904 (2022). https://doi.org/10.1007/s11704-021-1118-x

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