MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03248nab|a22004097a|4500 |
| 001 - CONTROL NUMBER |
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| control field |
65752 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
MX-TxCIM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240919021233.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
20228s2022||||mx |||p|op||||00||0|eng|d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
2073-4425 (Online) |
| 024 8# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
https://doi.org/10.3390/genes13081494 |
| 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 |
Montesinos-Lopez, O.A. |
| Field link and sequence number |
I1706800 |
| 9 (RLIN) |
2700 |
| Miscellaneous information |
Genetic Resources Program |
| 245 12 - TITLE STATEMENT |
|---|
| Title |
A comparison of three machine learning methods for multivariate genomic prediction using the sparse kernels method (SKM) library |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc. |
MDPI, |
| Date of publication, distribution, etc. |
2022. |
| Place of publication, distribution, etc. |
Basel (Switzerland) : |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Peer review |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Open Access |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Genomic selection (GS) changed the way plant breeders select genotypes. GS takes advantage of phenotypic and genotypic information to training a statistical machine learning model, which is used to predict phenotypic (or breeding) values of new lines for which only genotypic information is available. Therefore, many statistical machine learning methods have been proposed for this task. Multi-trait (MT) genomic prediction models take advantage of correlated traits to improve prediction accuracy. Therefore, some multivariate statistical machine learning methods are popular for GS. In this paper, we compare the prediction performance of three MT methods: the MT genomic best linear unbiased predictor (GBLUP), the MT partial least squares (PLS) and the multi-trait random forest (RF) methods. Benchmarking was performed with six real datasets. We found that the three investigated methods produce similar results, but under predictors with genotype (G) and environment (E), that is, E + G, the MT GBLUP achieved superior performance, whereas under predictors E + G + genotype (Formula presented.) environment (GE) and G + GE, random forest achieved the best results. We also found that the best predictions were achieved under the predictors E + G and E + G + GE. Here, we also provide the R code for the implementation of these three statistical machine learning methods in the sparse kernel method (SKM) library, which offers not only options for single-trait prediction with various statistical machine learning methods but also some options for MT predictions that can help to capture improved complex patterns in datasets that are common in genomic selection. |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 591 ## - CATALOGING NOTES |
|---|
| Affiliation |
Montesinos-Lopez, O.A. : No CIMMYT Affiliation |
| 591 ## - CATALOGING NOTES |
|---|
| Affiliation |
Hernández-Suárez, C.M. : Not in IRS staff list but CIMMYT Affiliation |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Genotypes |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1134 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Kernels |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
1168 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
11127 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Plant breeding |
| Miscellaneous information |
AGROVOC |
| Source of heading or term |
|
| 9 (RLIN) |
1203 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Forecasting |
| Source of heading or term |
AGROVOC |
| 9 (RLIN) |
2701 |
| 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 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Montesinos-Lopez, A. |
| 9 (RLIN) |
2702 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Cano-Paez, B. |
| 9 (RLIN) |
29337 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Hernández Suárez, C.M. |
| 9 (RLIN) |
521 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Santana-Mancilla, P.C. |
| 9 (RLIN) |
17803 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Crossa, J. |
| Miscellaneous information |
Genetic Resources Program |
| Field link and sequence number |
CCJL01 |
| 9 (RLIN) |
59 |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Genes |
| Related parts |
v. 13, no. 8, art. 1494 |
| Place, publisher, and date of publication |
Basel (Switzerland) : MDPI, 2022. |
| International Standard Serial Number |
2073-4425 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
Open Access through DSpace |
| Uniform Resource Identifier |
https://hdl.handle.net/10883/22288 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Article |
| Suppress in OPAC |
No |
| Source of classification or shelving scheme |
Dewey Decimal Classification |