Special Protein Molecules Computational Identification (Record no. 78926)

MARC details
000 -LEADER
fixed length control field 02722naaaa2200385uu 4500
001 - CONTROL NUMBER
control field https://directory.doabooks.org/handle/20.500.12854/59807
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220220093414.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783038970439
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783038970446
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title English
042 ## - AUTHENTICATION CODE
Authentication code dc
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Quan Zou (Ed.)
Relationship auth
245 10 - TITLE STATEMENT
Title Special Protein Molecules Computational Identification
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. MDPI - Multidisciplinary Digital Publishing Institute
Date of publication, distribution, etc. 2018
300 ## - PHYSICAL DESCRIPTION
Extent 1 electronic resource (VIII, 296 p.)
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open Access
Source of term star
Standardized terminology for access restriction Unrestricted online access
520 ## - SUMMARY, ETC.
Summary, etc. 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.
540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE
Terms governing use and reproduction Creative Commons
Use and reproduction rights https://creativecommons.org/licenses/by-nc-nd/4.0/
Source of term cc
-- https://creativecommons.org/licenses/by-nc-nd/4.0/
546 ## - LANGUAGE NOTE
Language note English
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term MHC binding peptide
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Uncontrolled term type III secreted proteins
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term machine learning
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Uncontrolled term oncogene
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Uncontrolled term anticancer peptides
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Uncontrolled term bioinformatics
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Uncontrolled term Proteomics
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Uncontrolled term DNA/RNA binding proteins
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Uncontrolled term prediction
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Uncontrolled term PseAAC features
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Uncontrolled term Cell-Penetrating Peptides
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term protein classification
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Uncontrolled term feature selection
856 40 - ELECTRONIC LOCATION AND ACCESS
Host name www.oapen.org
Uniform Resource Identifier <a href="http://www.mdpi.com/books/pdfview/book/697">http://www.mdpi.com/books/pdfview/book/697</a>
Access status 0
Public note DOAB: download the publication
856 40 - ELECTRONIC LOCATION AND ACCESS
Host name www.oapen.org
Uniform Resource Identifier <a href="https://directory.doabooks.org/handle/20.500.12854/59807">https://directory.doabooks.org/handle/20.500.12854/59807</a>
Access status 0
Public note DOAB: description of the publication

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