Identification of DNA-binding protein based multiple kernel model
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 Machi...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
AIMS Press
2023-06-01
|
Series: | Mathematical Biosciences and Engineering |
Subjects: | |
Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023586?viewType=HTML |
_version_ | 1797793970674204672 |
---|---|
author | Yuqing Qian Tingting Shang Fei Guo Chunliang Wang Zhiming Cui Yijie Ding Hongjie Wu |
author_facet | Yuqing Qian Tingting Shang Fei Guo Chunliang Wang Zhiming Cui Yijie Ding Hongjie Wu |
author_sort | Yuqing Qian |
collection | DOAJ |
description | 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/. |
first_indexed | 2024-03-13T02:55:57Z |
format | Article |
id | doaj.art-dbc1cea8e5e5400784f34091b2a81b2f |
institution | Directory Open Access Journal |
issn | 1551-0018 |
language | English |
last_indexed | 2024-03-13T02:55:57Z |
publishDate | 2023-06-01 |
publisher | AIMS Press |
record_format | Article |
series | Mathematical Biosciences and Engineering |
spelling | doaj.art-dbc1cea8e5e5400784f34091b2a81b2f2023-06-28T06:28:11ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-06-01207131491317010.3934/mbe.2023586Identification of DNA-binding protein based multiple kernel modelYuqing Qian0Tingting Shang1Fei Guo 2Chunliang Wang3Zhiming Cui 4Yijie Ding5Hongjie Wu 61. College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China1. College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China2. School of Computer Science and Engineering, Central South University, Changsha, China3. The Second Affiliated Hospital of Soochow University, Suzhou, China1. College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China4. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China1. College of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, ChinaDNA-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/.https://www.aimspress.com/article/doi/10.3934/mbe.2023586?viewType=HTMLdna-binding proteinsmultiple kernel learninglocal kernel alignmentrestricted kernel machine |
spellingShingle | Yuqing Qian Tingting Shang Fei Guo Chunliang Wang Zhiming Cui Yijie Ding Hongjie Wu Identification of DNA-binding protein based multiple kernel model Mathematical Biosciences and Engineering dna-binding proteins multiple kernel learning local kernel alignment restricted kernel machine |
title | Identification of DNA-binding protein based multiple kernel model |
title_full | Identification of DNA-binding protein based multiple kernel model |
title_fullStr | Identification of DNA-binding protein based multiple kernel model |
title_full_unstemmed | Identification of DNA-binding protein based multiple kernel model |
title_short | Identification of DNA-binding protein based multiple kernel model |
title_sort | identification of dna binding protein based multiple kernel model |
topic | dna-binding proteins multiple kernel learning local kernel alignment restricted kernel machine |
url | https://www.aimspress.com/article/doi/10.3934/mbe.2023586?viewType=HTML |
work_keys_str_mv | AT yuqingqian identificationofdnabindingproteinbasedmultiplekernelmodel AT tingtingshang identificationofdnabindingproteinbasedmultiplekernelmodel AT feiguo identificationofdnabindingproteinbasedmultiplekernelmodel AT chunliangwang identificationofdnabindingproteinbasedmultiplekernelmodel AT zhimingcui identificationofdnabindingproteinbasedmultiplekernelmodel AT yijieding identificationofdnabindingproteinbasedmultiplekernelmodel AT hongjiewu identificationofdnabindingproteinbasedmultiplekernelmodel |