Special Protein Molecules Computational Identification
It is time consuming and costly to detect new molecules of some special proteins. These special proteins include cytokines, enzymes, cell-penetrating peptides, anticancer peptides, cancer lectins, G-protein-coupled receptors, etc. Researchers often employ computer programs to list some candidates, a...
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| Định dạng: | Online |
| Ngôn ngữ: | Tiếng Anh |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Truy cập trực tuyến: | 27444 |
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| _version_ | 1869526171148877824 |
|---|---|
| author | Quan Zou (Ed.) |
| author_browse | Quan Zou (Ed.) |
| author_facet | Quan Zou (Ed.) |
| author_sort | Quan Zou (Ed.) |
| collection | Directory of Open Access Books |
| description | It is time consuming and costly to detect new molecules of some special proteins. These special proteins include cytokines, enzymes, cell-penetrating peptides, anticancer peptides, cancer lectins, G-protein-coupled receptors, etc. Researchers often employ computer programs to list some candidates, and to validate the candidates with molecular experiments. These computer programs are key to possible savings on wet experiment costs. Software results with high false positive will lead to high costs in the validation process. In this Special Issue, we focus on these computer program approaches and algorithms. Some "golden features" from protein primary sequences have been proposed for these problems, such as Chou’s PseAAC (pseudo amino acid composition). PseAAC has been tried on nearly all kinds of protein identification, together with SVM (support vector machines, a type of classifier). However, I prefer special features, and classification methods should be proposed for special protein molecules. "Golden features" cannot work well on all kinds of proteins. I hope that submissions will focus on a type of special protein molecule, collect related data sets, obtain better prediction performance (especially low false positives), and develop user-friendly software tools or web servers. |
| format | Online |
| id | doab-20.500.12854ir-59807 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-598072022-01-31T13:40:45Z Special Protein Molecules Computational Identification Quan Zou (Ed.) R858-859.7 MHC binding peptide type III secreted proteins machine learning oncogene anticancer peptides bioinformatics Proteomics DNA/RNA binding proteins prediction PseAAC features Cell-Penetrating Peptides protein classification feature selection It is time consuming and costly to detect new molecules of some special proteins. These special proteins include cytokines, enzymes, cell-penetrating peptides, anticancer peptides, cancer lectins, G-protein-coupled receptors, etc. Researchers often employ computer programs to list some candidates, and to validate the candidates with molecular experiments. These computer programs are key to possible savings on wet experiment costs. Software results with high false positive will lead to high costs in the validation process. In this Special Issue, we focus on these computer program approaches and algorithms. Some "golden features" from protein primary sequences have been proposed for these problems, such as Chou’s PseAAC (pseudo amino acid composition). PseAAC has been tried on nearly all kinds of protein identification, together with SVM (support vector machines, a type of classifier). However, I prefer special features, and classification methods should be proposed for special protein molecules. "Golden features" cannot work well on all kinds of proteins. I hope that submissions will focus on a type of special protein molecule, collect related data sets, obtain better prediction performance (especially low false positives), and develop user-friendly software tools or web servers. 2021-02-12T04:14:07Z 2021-02-12T04:14:07Z 2018-08-09 12:10:11 2018 book 27444 9783038970439 9783038970446 https://directory.doabooks.org/handle/20.500.12854/59807 eng image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International http://www.mdpi.com/books/pdfview/book/697 http://www.mdpi.com/books/pdfview/book/697 MDPI - Multidisciplinary Digital Publishing Institute 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783038970439 9783038970446 VIII, 296 open access |
| spellingShingle | R858-859.7 MHC binding peptide type III secreted proteins machine learning oncogene anticancer peptides bioinformatics Proteomics DNA/RNA binding proteins prediction PseAAC features Cell-Penetrating Peptides protein classification feature selection Quan Zou (Ed.) Special Protein Molecules Computational Identification |
| title | Special Protein Molecules Computational Identification |
| title_full | Special Protein Molecules Computational Identification |
| title_fullStr | Special Protein Molecules Computational Identification |
| title_full_unstemmed | Special Protein Molecules Computational Identification |
| title_short | Special Protein Molecules Computational Identification |
| title_sort | special protein molecules computational identification |
| topic | R858-859.7 MHC binding peptide type III secreted proteins machine learning oncogene anticancer peptides bioinformatics Proteomics DNA/RNA binding proteins prediction PseAAC features Cell-Penetrating Peptides protein classification feature selection |
| topic_facet | R858-859.7 MHC binding peptide type III secreted proteins machine learning oncogene anticancer peptides bioinformatics Proteomics DNA/RNA binding proteins prediction PseAAC features Cell-Penetrating Peptides protein classification feature selection |
| url | 27444 |
| work_keys_str_mv | AT quanzoued specialproteinmoleculescomputationalidentification |