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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| Vydáno v: | Frontiers in bioengineering and biotechnology Ročník 8; s. 635 |
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Frontiers Media S.A
25.06.2020
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| ISSN: | 2296-4185, 2296-4185 |
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| Abstract | 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.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. |
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| AbstractList | 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. 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.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. |
| Author | Wang, Donghua Chen, Zhihua Gu, Xingyue |
| AuthorAffiliation | 2 Department of General Surgery, Heilongjiang Province Land Reclamation Headquarters General Hospital , Harbin , China 1 Institute of Computing Science and Technology, Guangzhou University , Guangzhou , China |
| AuthorAffiliation_xml | – name: 1 Institute of Computing Science and Technology, Guangzhou University , Guangzhou , China – name: 2 Department of General Surgery, Heilongjiang Province Land Reclamation Headquarters General Hospital , Harbin , China |
| Author_xml | – sequence: 1 givenname: Xingyue surname: Gu fullname: Gu, Xingyue – sequence: 2 givenname: Zhihua surname: Chen fullname: Chen, Zhihua – sequence: 3 givenname: Donghua surname: Wang fullname: Wang, Donghua |
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| CitedBy_id | crossref_primary_10_3390_molecules28237865 crossref_primary_10_1016_j_vaccine_2024_04_078 crossref_primary_10_1109_TCBB_2021_3079116 crossref_primary_10_1080_07391102_2023_2269280 crossref_primary_10_3389_fgene_2022_1007618 crossref_primary_10_1016_j_fm_2024_104551 crossref_primary_10_1155_2022_7518779 crossref_primary_10_1016_j_chemolab_2022_104729 crossref_primary_10_1111_bph_16140 |
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| Snippet | 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... |
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| Title | Prediction of G Protein-Coupled Receptors With CTDC Extraction and MRMD2.0 Dimension-Reduction Methods |
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