-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathpaper.bib
124 lines (113 loc) · 6.69 KB
/
paper.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
@article{morota_shinygpas_2017,
title = {{ShinyGPAS}: interactive genomic prediction accuracy simulator based on deterministic formulas},
volume = {49},
issn = {1297-9686},
shorttitle = {{ShinyGPAS}},
url = {https://gsejournal.biomedcentral.com/articles/10.1186/s12711-017-0368-4},
doi = {10.1186/s12711-017-0368-4},
abstract = {Background: Deterministic formulas for the accuracy of genomic predictions highlight the relationships among prediction accuracy and potential factors influencing prediction accuracy prior to performing computationally intensive cross-validation. Visualizing such deterministic formulas in an interactive manner may lead to a better understanding of how genetic factors control prediction accuracy.
Results: The software to simulate deterministic formulas for genomic prediction accuracy was implemented in R and encapsulated as a web-based Shiny application. Shiny genomic prediction accuracy simulator (ShinyGPAS) simulates various deterministic formulas and delivers dynamic scatter plots of prediction accuracy versus genetic factors impacting prediction accuracy, while requiring only mouse navigation in a web browser. ShinyGPAS is available at: https://chikudaisei.shinyapps.io/shinygpas/.
Conclusion: ShinyGPAS is a shiny-based interactive genomic prediction accuracy simulator using deterministic formulas. It can be used for interactively exploring potential factors that influence prediction accuracy in genomeenabled prediction, simulating achievable prediction accuracy prior to genotyping individuals, or supporting in-class teaching. ShinyGPAS is open source software and it is hosted online as a freely available web-based resource with an intuitive graphical user interface.},
language = {en},
number = {1},
urldate = {2021-03-25},
journal = {Genetics Selection Evolution},
author = {Morota, Gota},
month = dec,
year = {2017},
pages = {91},
file = {Morota - 2017 - ShinyGPAS interactive genomic prediction accuracy.pdf:/Users/jchen/Zotero/storage/MCKGUZUS/Morota - 2017 - ShinyGPAS interactive genomic prediction accuracy.pdf:application/pdf},
}
@book{mrode_linear_2013,
address = {Boston, MA},
edition = {3rd ed},
title = {Linear models for the prediction of animal breeding values},
isbn = {978-1-84593-981-6 978-1-78064-391-5},
language = {en},
publisher = {CABI},
author = {Mrode, R. A.},
year = {2013},
keywords = {Breeding Mathematical models, Breeding Statistical methods, Genetics Mathematical models, Genetics Statistical methods, Livestock},
file = {Mrode - 2013 - Linear models for the prediction of animal breedin.pdf:/Users/jchen/Zotero/storage/49E8897S/Mrode - 2013 - Linear models for the prediction of animal breedin.pdf:application/pdf},
}
@misc{pinheiro_nlme_2021,
title = {nlme: {Linear} and {Nonlinear} {Mixed} {Effects} {Models}},
url = {https://CRAN.R-project.org/package=nlme},
author = {Pinheiro, Jose and Bates, Douglas and DebRoy, Saikat and Sarkar, Deepayan and {R Core Team}},
year = {2021},
keywords = {type: software},
}
@article{bates_fitting_2015,
title = {Fitting {Linear} {Mixed}-{Effects} {Models} {Using} lme4},
volume = {67},
doi = {10.18637/jss.v067.i01},
number = {1},
journal = {Journal of Statistical Software},
author = {Bates, Douglas and Mächler, Martin and Bolker, Ben and Walker, Steve},
year = {2015},
keywords = {type: software},
pages = {1--48},
}
@article{perez_genome-wide_2014,
title = {Genome-{Wide} {Regression} and {Prediction} with the {BGLR} {Statistical} {Package}},
volume = {198},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4196607/},
number = {2},
journal = {Genetics},
author = {Perez, Paulino and Campos, Gustavo de los},
year = {2014},
pages = {483--495},
}
@article{madsen_dmu_2006,
title = {{DMU} - a package for analyzing multivariate mixed models.},
url = {https://www.cabdirect.org/cabdirect/abstract/20063170093},
abstract = {DMU is a powerful tool for estimation of (co)variance components and QTL effects, and for prediction of breeding values for (normal, threshold and survival traits), using various statistical models. Several computing methods are built into the package and the choice of method depends on the actual analysis to perform. DMU is distributed free of charge for research purpose, but the use should be...},
language = {English},
urldate = {2021-05-05},
journal = {Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006},
author = {Madsen, P. and Sørensen, P. and Su, G. and Damgaard, L. H. and Thomsen, H. and Labouriau, R.},
year = {2006},
note = {Publisher: Instituto Prociência},
pages = {27--11},
file = {Snapshot:/Users/jchen/Zotero/storage/9U9HSAXH/20063170093.html:text/html},
}
@inproceedings{cheng_jwas_2018,
title = {{JWAS}: {Julia} implementation of whole-genome analysis software},
volume = {11},
shorttitle = {{JWAS}},
booktitle = {Proceedings of the world congress on genetics applied to livestock production},
author = {Cheng, Hao and Fernando, Rohan and Garrick, Dorian},
year = {2018},
keywords = {type: software},
pages = {859},
}
@misc{noauthor_26th_nodate,
title = {26th {Summer} {Institute} in {Statistical} {Genetics} ({SISG}) {\textbar} {Summer} {Institutes}},
url = {https://si.biostat.washington.edu/suminst/sisg},
urldate = {2021-05-05},
file = {26th Summer Institute in Statistical Genetics (SISG) | Summer Institutes:/Users/jchen/Zotero/storage/ZUGX92HZ/sisg.html:text/html},
}
@misc{misztal_manual_2018,
title = {Manual for {BLUPF90} family programs},
publisher = {University of Georgia},
author = {Misztal and Tsuruta, I. S. and Lourenco, D. A. L. and Masuda, Y. and Aguilar, I. and Legarra, A. and Vitezica, , Z},
year = {2018},
}
@misc{bokeh_development_team_bokeh_2018,
title = {Bokeh: {Python} library for interactive visualization},
url = {https://bokeh.pydata.org/en/latest/},
author = {{Bokeh Development Team}},
year = {2018},
}
@misc{takafumi_arakaki_juliapypyjulia_2020,
title = {{JuliaPy}/pyjulia: {PyJulia} v0.5.6},
shorttitle = {{JuliaPy}/pyjulia},
url = {https://zenodo.org/record/4294940#.YJLVqmZKhb8},
abstract = {python interface to julia},
urldate = {2021-05-05},
author = {Takafumi Arakaki and Jake Bolewski and Robin Deits and Keno Fischer and Steven G. Johnson and Matthias Bussonnier and Isaiah Norton and Páll Haraldsson and Matthew Rocklin and Tsur and Viral B. Shah and Daniel Soto and eslgastal and Elias Kuthe and jakirkham and Marius Millea and grahamgill and fnmdx111 and Alex Arslan and Darren Christopher Lukas and David Nadlinger and Lilian Besson and Sheehan Olver and Taine Zhao and scls19fr},
month = nov,
year = {2020},
doi = {10.5281/zenodo.4294940},
file = {Zenodo Snapshot:/Users/jchen/Zotero/storage/HLR98NGS/4294940.html:text/html},
}