Research Area:  Machine Learning

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.

Keywords:  
String Kernels Construction
Fusion
Bioinformatics Application
Deep Learning
Machine Learning

Author(s) Name:  Ren Qi, Fei Guo & Quan Zou

Publisher name:  Springer

DOI:  10.1007/s11704-021-1118-x

Volume Information:  volume 16, Article number: 166904 (2022)

Paper Link:   https://link.springer.com/article/10.1007/s11704-021-1118-x