| 000 | 02722naaaa2200385uu 4500 | ||
|---|---|---|---|
| 001 | https://directory.doabooks.org/handle/20.500.12854/59807 | ||
| 005 | 20220220093414.0 | ||
| 020 | _a9783038970439 | ||
| 020 | _a9783038970446 | ||
| 041 | 0 | _aEnglish | |
| 042 | _adc | ||
| 100 | 1 |
_aQuan Zou (Ed.) _4auth |
|
| 245 | 1 | 0 | _aSpecial Protein Molecules Computational Identification |
| 260 |
_bMDPI - Multidisciplinary Digital Publishing Institute _c2018 |
||
| 300 | _a1 electronic resource (VIII, 296 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aIt 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. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by-nc-nd/4.0/ _2cc _4https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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| 546 | _aEnglish | ||
| 653 | _aMHC binding peptide | ||
| 653 | _atype III secreted proteins | ||
| 653 | _amachine learning | ||
| 653 | _aoncogene | ||
| 653 | _aanticancer peptides | ||
| 653 | _abioinformatics | ||
| 653 | _aProteomics | ||
| 653 | _aDNA/RNA binding proteins | ||
| 653 | _aprediction | ||
| 653 | _aPseAAC features | ||
| 653 | _aCell-Penetrating Peptides | ||
| 653 | _aprotein classification | ||
| 653 | _afeature selection | ||
| 856 | 4 | 0 |
_awww.oapen.org _uhttp://www.mdpi.com/books/pdfview/book/697 _70 _zDOAB: download the publication |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/59807 _70 _zDOAB: description of the publication |
| 999 |
_c78926 _d78926 |
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