Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods
The G Protein-Coupled Receptor (GPCR) family consists of more than 800 different members. In this article, we attempt to use the physicochemical properties of Composition, Transition, Distribution (CTD) to represent GPCRs. The dimensionality reduction method of MRMD2.0 filters the physicochemical pr...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-06-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2020.00635/full |
| _version_ | 1819001693170302976 |
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| author | Xingyue Gu Zhihua Chen Donghua Wang |
| author_facet | Xingyue Gu Zhihua Chen Donghua Wang |
| author_sort | Xingyue Gu |
| collection | DOAJ |
| description | The G Protein-Coupled Receptor (GPCR) family consists of more than 800 different members. In this article, we attempt to use the physicochemical properties of Composition, Transition, Distribution (CTD) to represent GPCRs. The dimensionality reduction method of MRMD2.0 filters the physicochemical properties of GPCR redundancy. Matplotlib plots the coordinates to distinguish GPCRs from other protein sequences. The chart data show a clear distinction effect, and there is a well-defined boundary between the two. The experimental results show that our method can predict GPCRs. |
| first_indexed | 2024-12-20T22:53:16Z |
| format | Article |
| id | doaj.art-b10a8ab1a2a449e598198e52a97f6d2f |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-12-20T22:53:16Z |
| publishDate | 2020-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-b10a8ab1a2a449e598198e52a97f6d2f2022-12-21T19:24:12ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852020-06-01810.3389/fbioe.2020.00635554076Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction MethodsXingyue Gu0Zhihua Chen1Donghua Wang2Institute of Computing Science and Technology, Guangzhou University, Guangzhou, ChinaInstitute of Computing Science and Technology, Guangzhou University, Guangzhou, ChinaDepartment of General Surgery, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, ChinaThe G Protein-Coupled Receptor (GPCR) family consists of more than 800 different members. In this article, we attempt to use the physicochemical properties of Composition, Transition, Distribution (CTD) to represent GPCRs. The dimensionality reduction method of MRMD2.0 filters the physicochemical properties of GPCR redundancy. Matplotlib plots the coordinates to distinguish GPCRs from other protein sequences. The chart data show a clear distinction effect, and there is a well-defined boundary between the two. The experimental results show that our method can predict GPCRs.https://www.frontiersin.org/article/10.3389/fbioe.2020.00635/fullfeature extractionCTDMRMD2.0Matplotlibpredict GPCRs |
| spellingShingle | Xingyue Gu Zhihua Chen Donghua Wang Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods Frontiers in Bioengineering and Biotechnology feature extraction CTD MRMD2.0 Matplotlib predict GPCRs |
| title | Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods |
| title_full | Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods |
| title_fullStr | Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods |
| title_full_unstemmed | Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods |
| title_short | Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods |
| title_sort | prediction of g protein coupled receptors with ctdc extraction and mrmd2 0 dimension reduction methods |
| topic | feature extraction CTD MRMD2.0 Matplotlib predict GPCRs |
| url | https://www.frontiersin.org/article/10.3389/fbioe.2020.00635/full |
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