Yujian Yuan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yujian Yuan has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Lina Yang
School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China
1 shared publication - Wei Li
School of Computer, Electronics and Information, Guangxi University, Nanning, China
1 shared publication - Xichun Li
The School of Mathematics and Computer Science, Guangxi Minzu Normal University, Chongzuo, China
1 shared publication - Yu Qiu
School of Computer, Electronics and Information, Guangxi University, Nanning, China
1 shared publication - Zuqiang Meng
School of Computer Science and Electronic Engineering, Guangxi University, Nanning 530004, China
1 shared publication
Latest Publications
- Open Access
- 19 August 2022
- Published by Springer Nature in BMC Bioinformatics
- Vol. 23 (1) , 1-21
FFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis
Amino acid property-aware phylogenetic analysis (APPA) refers to the phylogenetic analysis method based on amino acid property encoding, which is used for understanding and inferring evolutionary relationships between species from the molecular perspective. Fast Fourier transform (FFT) and Higuchi’s fractal dimension (HFD) have excellent performance in describing sequences’ structural and complexity information for APPA. However, with the exponential growth of protein sequence data, it is very important to develop a reliable APPA method for protein sequence analysis. Consequently, we propose a new method named FFP, it joints FFT and HFD. Firstly, FFP is used to encode protein sequences on the basis of the important physicochemical properties of amino acids, the dissociation constant, which determines acidity and basicity of protein molecules. Secondly, FFT and HFD are used to generate the feature vectors of encoded sequences, whereafter, the distance matrix is calculated from the cosine function, which describes the degree of similarity between species. The smaller the distance between them, the more similar they are. Finally, the phylogenetic tree is constructed. When FFP is tested for phylogenetic analysis on four groups of protein sequences, the results are obviously better than other comparisons, with the highest accuracy up to more than 97%. FFP has higher accuracy in APPA and multi-sequence alignment. It also can measure the protein sequence similarity effectively. And it is hoped to play a role in APPA’s related research.
- 19 August 2022
- Published by Springer Nature in BMC Bioinformatics
- Vol. 23 (1) , 1-21
Open AccessFFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis
Amino acid property-aware phylogenetic analysis (APPA) refers to the phylogenetic analysis method based on amino acid property encoding, which is used for understanding and inferring evolutionary relationships between species from the molecular perspective. Fast Fourier transform (FFT) and Higuchi’s fractal dimension (HFD) have excellent performance in describing sequences’ structural and complexity information for APPA. However, with the exponential growth of protein sequence data, it is very important to develop a reliable APPA method for protein sequence analysis. Consequently, we propose a new method named FFP, it joints FFT and HFD. Firstly, FFP is used to encode protein sequences on the basis of the important physicochemical properties of amino acids, the dissociation constant, which determines acidity and basicity of protein molecules. Secondly, FFT and HFD are used to generate the feature vectors of encoded sequences, whereafter, the distance matrix is calculated from the cosine function, which describes the degree of similarity between species. The smaller the distance between them, the more similar they are. Finally, the phylogenetic tree is constructed. When FFP is tested for phylogenetic analysis on four groups of protein sequences, the results are obviously better than other comparisons, with the highest accuracy up to more than 97%. FFP has higher accuracy in APPA and multi-sequence alignment. It also can measure the protein sequence similarity effectively. And it is hoped to play a role in APPA’s related research.