Identification of DNA-binding protein based multiple kernel model-Reference-Cited by-同舟云学术

Identification of DNA-binding protein based multiple kernel model

Published:2023 Issue:7 Volume:20 Page:13149-13170
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Qian Yuqing¹,Shang Tingting¹,Guo Fei²,Wang Chunliang³,Cui Zhiming¹,Ding Yijie⁴,Wu Hongjie¹

Affiliation:

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

Abstract

<abstract> <p>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 <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://8.130.69.121:8082/">http://8.130.69.121:8082/</ext-link>.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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