Identification of DNA-binding protein based multiple kernel model

Yuqing Qian; Tingting Shang; Fei Guo; Chunliang Wang; Zhiming Cui; Yijie Ding; Hongjie Wu

doi:10.3934/mbe.2023586

Identification of DNA-binding protein based multiple kernel model

Math Biosci Eng. 2023 Jun 6;20(7):13149-13170. doi: 10.3934/mbe.2023586.

Authors

Yuqing Qian¹, Tingting Shang¹, Fei Guo², Chunliang Wang³, Zhiming Cui¹, Yijie Ding⁴, Hongjie Wu¹

Affiliations

¹ College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
² School of Computer Science and Engineering, Central South University, Changsha, China.
³ The Second Affiliated Hospital of Soochow University, Suzhou, China.
⁴ Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.

PMID: 37501482
DOI: 10.3934/mbe.2023586

Abstract

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/.

Keywords: DNA-binding proteins; local kernel alignment; multiple kernel learning; restricted kernel machine.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
DNA-Binding Proteins*
Support Vector Machine

Substances

DNA-Binding Proteins