ORCID Profile
0000-0003-2911-7643
Current Organisations
Tianjin University
,
University of Electronic Science and Technology of China
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.NEUNET.2022.09.026
Abstract: Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs is a necessary way to study ncRNA. Traditional biochemical methods are time-consuming and labor-intensive, and computational-based methods can help detect the location of ncRNAs on a large scale. However, many models did not consider the correlation information among multiple subcellular localizations of ncRNAs. This study proposes a radial basis function neural network based on shared subspace learning (RBFNN-SSL), which extract shared structures in multi-labels. To evaluate performance, our classifier is tested on three ncRNA datasets. Our model achieves better performance in experimental results.
Publisher: Elsevier BV
Date: 12-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Elsevier BV
Date: 12-2022
Publisher: Elsevier BV
Date: 08-2022
Publisher: Elsevier BV
Date: 2024
Publisher: Elsevier BV
Date: 06-2023
Publisher: Elsevier BV
Date: 03-2023
Publisher: Elsevier BV
Date: 12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.COMPBIOMED.2022.106268
Abstract: DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase, prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of the conventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years, a variety of machine learning-based methods that have been used on a large scale for large-scale screening of DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparse representation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solve nonlinear classification problems. And L
Location: China
No related grants have been discovered for Yijie Ding.