Knowledge Center Catalog

The look ahead trace back optimizer for genomic selection under transparent and opaque simulators (Record no. 64145)

MARC details
000 -LEADER
fixed length control field 00595nab|a22002177a|4500
001 - CONTROL NUMBER
control field 64145
003 - CONTROL NUMBER IDENTIFIER
control field MX-TxCIM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20210902155733.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201209s2021||||xxk|||p|op||||00||0|eng|d
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2045-2322
024 8# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1038/s41598-021-83567-5
040 ## - CATALOGING SOURCE
Original cataloging agency MX-TxCIM
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Amini, F.
9 (RLIN) 22615
245 14 - TITLE STATEMENT
Title The look ahead trace back optimizer for genomic selection under transparent and opaque simulators
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. London (United Kingdom) :
Name of publisher, distributor, etc. Nature Publishing Group,
Date of publication, distribution, etc. 2021.
500 ## - GENERAL NOTE
General note Peer review
500 ## - GENERAL NOTE
General note Open Access
520 ## - SUMMARY, ETC.
Summary, etc. Recent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.
546 ## - LANGUAGE NOTE
Language note Text in English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Marker-assisted selection
Source of heading or term AGROVOC
9 (RLIN) 10737
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Simulation
Source of heading or term AGROVOC
9 (RLIN) 8687
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Biotechnology
Source of heading or term AGROVOC
9 (RLIN) 5143
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Bioinformatics
Source of heading or term AGROVOC
9 (RLIN) 8703
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Franco, F.R.
9 (RLIN) 22616
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Guiping Hu
9 (RLIN) 22617
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Lizhi Wang
9 (RLIN) 22618
773 0# - HOST ITEM ENTRY
Related parts v. 11, art. 4124
Place, publisher, and date of publication London (United Kingdom) : Nature Publishing Group, 2021.
International Standard Serial Number 2045-2322
Title Nature Scientific Reports
Record control number a58025
856 4# - ELECTRONIC LOCATION AND ACCESS
Link text Click here to access online
Uniform Resource Identifier https://doi.org/10.1038/s41598-021-83567-5
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Article
Suppress in OPAC No
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Date last seen Total Checkouts Price effective from Koha item type Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Withdrawn status Home library Current library Date acquired
08/30/2021   08/30/2021 Article Not Lost Dewey Decimal Classification     Reprints Collection   CIMMYT Knowledge Center: John Woolston Library CIMMYT Knowledge Center: John Woolston Library 08/30/2021

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