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...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
MDPI - Multidisciplinary Digital Publishing Institute
2018
MDPI |
| Témata: | |
| ISBN: | 3038970441, 9783038970439, 3038970433, 9783038970446 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | 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. |
|---|---|
| AbstractList | 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, 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. |
| Author | Quan Zou (Ed.) |
| Author_xml | – sequence: 1 fullname: Zou, Quan |
| BookMark | eNpNkN1KAzEQhQMqaGufQJC-wOpkk83PpRR_ChUFxdswSSYY3W7K7vbCt3dpvZC5OJyZw3dgZuy0Kx0xds3hRggLt76U78FqU4kKhLG6AikrdcJm4mAnx8_ZYhi-AKC2YKBuLph621HI2C5f-zJS7pbPpaWwb2lYrsp2tx9xzKWb7utI3ZhTDofFJTtL2A60-NM5-3i4f189VZuXx_XqblNhzSd-FRojbDAiENdAaNBHr2IMQqsUuTeKp2S0EqpGiKnGWpDkEkOtI1AjScyZO4J99m0uvmAfXdlR19NA2IfPNvse-x9XMLv_mVC2zgaRjAdykjfcyRSU8yDQJa0bOY3SyU4NV8eGghPWxYKHR7rGGtDiF9T_akI |
| ContentType | eBook |
| Copyright | https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode |
| Copyright_xml | – notice: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode |
| DBID | V1H BIANM |
| DOI | 10.3390/books978-3-03897-044-6 |
| DatabaseName | DOAB: Directory of Open Access Books Open Research Library (Open Access) |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: V1H name: DOAB: Directory of Open Access Books url: https://directory.doabooks.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| ExternalDocumentID | oai_biblioboard_com_9c3f8b0e_4151_4fc6_b03a_f775454567f9 59807 |
| GroupedDBID | AGWHU ALMA_UNASSIGNED_HOLDINGS BIANM HVQEU PYIOH V1H |
| ID | FETCH-LOGICAL-a21025-c5839c83ce170ea8abdb6ddc376fd1b861ff876362a0df2a23e414ac27d0e54e3 |
| IEDL.DBID | V1H |
| ISBN | 3038970441 9783038970439 3038970433 9783038970446 |
| IngestDate | Tue Dec 02 16:43:32 EST 2025 Wed Oct 08 01:53:57 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | false |
| LCCallNum_Ident | R858-859.7 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a21025-c5839c83ce170ea8abdb6ddc376fd1b861ff876362a0df2a23e414ac27d0e54e3 |
| Notes | MODID-565f2b0520d:MDPI |
| OpenAccessLink | https://directory.doabooks.org/handle/20.500.12854/59807 |
| PageCount | VIII, 296 |
| ParticipantIDs | biblioboard_openresearchlibrary_oai_biblioboard_com_9c3f8b0e_4151_4fc6_b03a_f775454567f9 oapen_doabooks_59807 |
| PublicationCentury | 2000 |
| PublicationDate | 2018 2018-01-01T00:00:00Z |
| PublicationDateYYYYMMDD | 2018-01-01 |
| PublicationDate_xml | – year: 2018 text: 2018 |
| PublicationDecade | 2010 |
| PublicationYear | 2018 |
| Publisher | MDPI - Multidisciplinary Digital Publishing Institute MDPI |
| Publisher_xml | – name: MDPI - Multidisciplinary Digital Publishing Institute – name: MDPI |
| SSID | ssj0002908025 |
| Score | 2.071682 |
| Snippet | It is time consuming and costly to detect new molecules of some special proteins. These special proteins include cytokines, enzymes, cell-penetrating peptides,... Special Protein Molecules Computational Identification |
| SourceID | biblioboard oapen |
| SourceType | Open Access Repository Publisher |
| SubjectTerms | anticancer peptides bioinformatics Cell-Penetrating Peptides DNA/RNA binding proteins feature selection machine learning MHC binding peptide oncogene prediction protein classification Proteomics PseAAC features R858-859.7 type III secreted proteins |
| Title | Special Protein Molecules Computational Identification |
| URI | https://directory.doabooks.org/handle/20.500.12854/59807 https://openresearchlibrary.org/viewer/9c3f8b0e-4151-4fc6-b03a-f775454567f9 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV27TsMwFLWqFiFYeBREeSkDq2ketmPPiKpLqw6o6mb5KSqhBLWF78c3TqsgMTE6TqTkxnHOvfY5B6En0GsBc1HsmGeYaKYwN1xhktuCcK8D5LWN2UQ5n_PVSix6iO-5MHEir2FXc60AZ26b5fwoORAy9WeagiICp2RMBQca-SBkOAzSrmU2PVRXcgEcUgpkDlCQK0Gmq9MgWdTeOXSK323CIpm4KEQ6bu4i7iJounHoxwEvn-q1_ljXug6vEuyS1KerOv-mydn_n-ocDRxwHS5Qz1WX6HjWrrMPEWtt6ZMFyDisq2QWXXTdNok2EG0JMYk8X98W_q7QcvL69jLFrcMCVpDqUWxoAEiGF8ZlZeoUV9pqZq0J0463meYs8x4061iuUutzlReOZESZvLSpo8QV16hf1ZW7QUmudKmcciJcBqJtwpowQMJcq1yeMUtHaNUJmARzsFbP6L0tWEmQuu6eE745KUzhuU6dDEgjk8QbJnVaKOlBug_gX-nFCA2b6Mt9dGUTxdu_D9-hk4B6eKyj3KP-bvPlHtCR-d6tt5vHZhz9AN_Ewwk |
| linkProvider | Open Access Publishing in European Networks |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.title=Special+Protein+Molecules+Computational+Identification&rft.au=Quan+Zou+%28Ed.%29&rft.date=2018-01-01&rft.pub=MDPI+-+Multidisciplinary+Digital+Publishing+Institute&rft.isbn=9783038970439&rft_id=info:doi/10.3390%2Fbooks978-3-03897-044-6&rft.externalDBID=V1H&rft.externalDocID=59807 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9783038970446/lc.gif&client=summon&freeimage=true |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9783038970446/mc.gif&client=summon&freeimage=true |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9783038970446/sc.gif&client=summon&freeimage=true |