| Sonstiges: |
- Nachgewiesen in: MEDLINE
- Sprachen: English
- Publication Type: Journal Article; Review
- Language: English
- [J Appl Genet] 2024 May; Vol. 65 (2), pp. 225-240. <i>Date of Electronic Publication: </i>2024 Jan 13.
- MeSH Terms: Artificial Intelligence* ; Machine Learning* ; Humans ; Reproducibility of Results ; Algorithms ; Genomics / methods
- References: Abrougui K, Gabsi K, Mercatoris B, Khemis C, Amami R, Chehaibi S (2019) Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR). Soil Tillage Res 190:202–208. (PMID: 10.1016/j.still.2019.01.011) ; Acquaah G (2007) Breeding self-pollinated species. Principles of plant genetics and breeding, 2nd edn. Wiley-Blackwell Publishing, Malden, MA, USA, pp 281–312. ; Aggarwal CC (2018) Neural networks and deep learning. Springer, Cham, p 457. (PMID: 10.1007/978-3-319-94463-0) ; Ahmad Y, Haider S, Iqbal J, Abbasi BA, Yaseen T, Mahmood T (2022) The mechanisms of genome editing technologies in crop plants. Principles and Practices of OMICS and Genome Editing for Crop Improvement. Springer International Publishing, Cham, pp 295–313. (PMID: 10.1007/978-3-030-96925-7_13) ; Ahmar S, Gill RA, Jung KH, Faheem A, Qasim MU, Mubeen M, Zhou W (2020) Conventional and molecular techniques from simple breeding to speed breeding in crop plants: recent advances and future outlook. Int J Mol Sci 21:2590. (PMID: 32276445717791710.3390/ijms21072590) ; Akdemir D, Sanchez JI, Jannink JL (2015) Optimization of genomic selection training populations with a genetic algorithm. Genet Sel Evol 47:38. https://doi.org/10.1186/s12711-015-0116-6. (PMID: 10.1186/s12711-015-0116-6259431054422310) ; Akohoue F, Achigan-Dako EG, Sneller C, Van Deynze A, Sibiya J (2020) Genetic diversity, SNP-trait associations and genomic selection accuracy in a west African collection of Kersting’s groundnut [Macrotyloma geocarpum (Harms) Mar’echal and Baudet]. PLoS One 15:e0234769. (PMID: 32603370732619510.1371/journal.pone.0234769) ; Alipanahi R, Safari L, Khanteymoori A (2023) CRISPR genome editing using computational approaches: a survey. Front Bioinform 2:1001131. (PMID: 36710911987588710.3389/fbinf.2022.1001131) ; Annicchiarico P, Nazzicari N, Pecetti L, Romani M, Russi L (2019) Pea genomic selection for Italian environments. BMC Genom 20:603. (PMID: 10.1186/s12864-019-5920-x) ; Araveeporn A (2021) The higher-order of adaptive lasso and elastic net methods for classification on high dimensional data. Mathematics 9:1091. (PMID: 10.3390/math9101091) ; Astivia OLO, Zumbo BD (2019) Heteroskedasticity in multiple regression analysis: what it is, how to detect it and how to solve it with applications in R and SPSS. Pract Assess Res Evaluation 24:1. ; Atanda SA, Olsen M, Burgueño J, Crossa J, Dzidzienyo D, Beyene Y, Robbins KR (2021) Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program. Theor App Genet 134:279–294. (PMID: 10.1007/s00122-020-03696-9) ; Azevedo CF, Nascimento M, Fontes VC, Silva FF, de Resende MDV, Cruz CD, Singh RP, Peña RJ, Dreisigacker S, Fritz AK, Poland JA (2019) Genomic land: software for genome-wide association studies and genomic prediction. Acta Sci Agron 41:e45361. (PMID: 10.4025/actasciagron.v41i1.45361) ; Bao XR, Pan Y, Lee CM, Davis TH, Bao G (2021) Tools for experimental and computational analyses of off-target editing by programmable nucleases. Nat Protoc 16:10–26. (PMID: 3328895310.1038/s41596-020-00431-y) ; Battenfield SD, Guzm’an C, Gaynor RC, Singh RP, Pe˜na RJ, Dreisigacker S, Fritz AK, Poland JA (2016) Genomic selection for processing and end-use quality traits in the CIMMYT spring bread wheat breeding program. Plant Genome 9:plantgenome2016.01.0005. https://doi.org/10.3835/plantgenome2016.01.0005. ; Bhat JA, Deshmukh R, Zhao T, Patil G, Deokar A, Shinde S, Chaudhary J (2020) Harnessing high-throughput phenotyping and genotyping for enhanced drought tolerance in crop plants. J Biotech 324:248–260. (PMID: 10.1016/j.jbiotec.2020.11.010) ; Bhatta M, Gutierrez L, Cammarota L, Cardozo F, Germán S, Gómez-Guerrero B, Pardo MF, Lanaro V, Sayas M, Castro AJ (2020) Multi-trait genomic prediction model increased the predictive ability for agronomic and malting quality traits in barley (Hordeum vulgare L) G3. Genes Genomes Genet 10:1113–1124. (PMID: 10.1534/g3.119.400968) ; Biazzi E, Nazzicari N, Pecetti L, Brummer EC, Palmonari A, Tava A, Annicchiarico P (2017) Genome-wide association mapping and genomic selection for alfalfa (Medicago sativa) forage quality traits. PLoS ONE 12:e0169234. (PMID: 28068350522237510.1371/journal.pone.0169234) ; Biddick M, Burns KC (2018) Phenotypic trait matching predicts the topology of an insular plant–bird pollination network. Integr Zool 13:339–347. (PMID: 2953772310.1111/1749-4877.12319) ; Blondel M, Onogi A, Iwata H, Ueda N (2015) A ranking approach to genomic selection. PLoS ONE 10:e0128570. (PMID: 26068103446677410.1371/journal.pone.0128570) ; Bolger AM, Poorter H, Dumschott K, Bolger ME, Arend D, Osorio S, Gundlach H, Mayer KF, Lange M, Scholz U, Usadel B (2019) Computational aspects underlying genome to phenome analysis in plants. Plant J 97:182–198. (PMID: 30500991684979010.1111/tpj.14179) ; Bouvet JM, Makouanzi G, Cros D, Vigneron PH (2016) Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications. Heredity 116:146–157. (PMID: 2632876010.1038/hdy.2015.78) ; Brummer EC, Barber WT, Collie SM, Cox TS, Johnson R, Murray SC, Olsen RT, Pratt RC, Thro AM (2011) Plant breeding for harmony between agriculture and the environment. Front Ecol Environ 9:561–568. (PMID: 10.1890/100225) ; Budhlakoti, M, Mishra, DCM, Rai, A (2019) R package MTGS: genomic selection using multiple traits https://CRANR-projectorg/package=MTGS. ; Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, Maurer HP, Reif JC, Würschum T, Müller J, Rahe F (2013) BreedVision—a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13:2830–2847. (PMID: 23447014365871710.3390/s130302830) ; Callaway E (2020) ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature 588:203–204. (PMID: 3325788910.1038/d41586-020-03348-4) ; Cao Q, Ma J, Chen CH, Xu H, Chen Z, Li W, Liu XS (2017) CRISPR-FOCUS: a web server for designing focused CRISPR screening experiments. PLoS ONE 12:e0184281. (PMID: 28873439558492210.1371/journal.pone.0184281) ; Charmet G, Tran LG, Auzanneau J, Rincent R, Bouchet S (2020) BWGS: a R package for genomic selection and its application to a wheat breeding programme. PLoS ONE 15:e0222733. (PMID: 32240182714141810.1371/journal.pone.0222733) ; Chien LC (2020) A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies. PLoS ONE 15:e0233847. (PMID: 32559184730461510.1371/journal.pone.0233847) ; Chuai G, Ma H, Yan J (2018) DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome Biol 19:1–18. (PMID: 10.1186/s13059-018-1459-4) ; Clark LV, Dwiyanti MS, Anzoua KG, Brummer JE, Ghimire BK, Głowacka K, Hall M, Heo K, Jin X, Lipka AE, Peng J, Yamada T, Yoo JH, Yu CY, Zhao H, Long SP, Sacks EJ (2019) Genome-wide association and genomic prediction for biomass yield in a genetically diverse Miscanthus sinensis germplasm panel phenotyped at five locations in Asia and North America. Glob Change Biol Bioenergy 11:988–1007. (PMID: 10.1111/gcbb.12620) ; Crossa J, de los Campos G, Maccaferri M, Tuberosa R, Burgueño J, Pérez-Rodríguez P (2016) Extending the marker × environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat. Crop Sci 56:2193–2209. (PMID: 10.2135/cropsci2015.04.0260) ; Cuevas J, Crossa J, Soberanis V, Pérez-Elizalde S, Pérez-Rodríguez P, de los Campos G, Montesinos-López OA, Burgueño J, Roo Q, Crossa J, Montesinos-López O, Burgueño J (2016) Genomic prediction of genotype environment interaction kernel regression models. Plant Genome 9:1–20. https://doi.org/10.3835/plantgenome2016.03.0024. (PMID: 10.3835/plantgenome2016.03.0024) ; Cyplik A, Piaskowska D, Czembor P, Bocianowski J (2023) The use of weighted multiple linear regression to estimate QTL× QTL× QTL interaction effects of winter wheat (Triticum aestivum L.) doubled-haploid lines. J Appl Genet 64:679–693. https://doi.org/10.1007/s13353-023-00795-3. (PMID: 10.1007/s13353-023-00795-33787816910632291) ; Danandeh Mehr A, Fathollahzadeh AN (2021) A gradient boosting tree approach for SPEI classification and prediction in Turkey. Hydrol Sci J 66:1653–1663. (PMID: 10.1080/02626667.2021.1962884) ; de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375–385. (PMID: 19293140267483410.1534/genetics.109.101501) ; de los Campos G, Gianola D, Rosa GJM, Weigel KA, Crossa J (2010) Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods. Genet Res 92:295–308. (PMID: 10.1017/S0016672310000285) ; de Oliveira EJ, de Resende MDV, da Silva SV, Ferreira CF, Oliveira GAF, da Silva MS, de Oliveira LA, Aguilar-Vildoso CI (2012) Genome-wide selection in cassava. Euphytica 187:263–276. (PMID: 10.1007/s10681-012-0722-0) ; de Oliveira AA, Pastina MM, de Souza VF, da Costa Parrella RA, Noda RW, Simeone MLF, Schaffert RE, de Magalhães JV, Damasceno CMB, Margarido GRA (2018) Genomic prediction applied to high-biomass sorghum for bioenergy production. Mol Breed 38:49. (PMID: 2967045710.1007/s11032-018-0802-5) ; Debnath S, Preetham A, Vuppu S, Kumar SNP (2023) Optimal weighted GAN and U-Net based segmentation for phenotypic trait estimation of crops using Taylor Coot algorithm. Appl Soft Comput 144:110396. (PMID: 10.1016/j.asoc.2023.110396) ; Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601. (PMID: 2497070710.1016/j.tplants.2014.05.006) ; Dhugga KS (2022) Gene editing to accelerate crop breeding. Front Plant Sci 13:889995. (PMID: 35712601919688110.3389/fpls.2022.889995) ; Duangjit J, Causse M, Sauvage C (2016) Efficiency of genomic selection for tomato fruit quality. Mol Breed 36:1–16. (PMID: 10.1007/s11032-016-0453-3) ; Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250–255. (PMID: 10.3835/plantgenome2011.08.0024) ; Endelman JB, Carley CAS, Bethke PC, Coombs JJ, Clough ME, da Silva WL, De Jong WS, Douches DS, Frederick CM, Haynes KG, Holm DG (2018) Genetic variance partitioning and genome-wide prediction with allele dosage information in autotetraploid potato. Genetics 209:77–87. (PMID: 29514860593717310.1534/genetics.118.300685) ; Eraslan G, Avsec Ž, Gagneur J, Theis FJ (2019) Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 20:389–403. (PMID: 3097180610.1038/s41576-019-0122-6) ; F`e D, Ashraf BH, Pedersen MG, Janss L, Byrne S, Roulund N, Lenk I, Didion T, Asp T, Jensen CS, Jensen J (2016) Accuracy of genomic prediction in a commercial perennial ryegrass breeding program. Plant Genome 9:plantgenome2015.11.0110. https://doi.org/10.3835/plantgenome2015.11.0110. ; Fernandes SB, Dias KO, Ferreira FD, Brown JP (2018) Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum. Theor Appl Genet 131:747–755. (PMID: 2921837810.1007/s00122-017-3033-y) ; Fiedler JD, Lanzatella C, Edm’e SJ, Palmer NA, Sarath G, Mitchell R, Tobias CM (2018) Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations. BMC Plant Biol 18:142. (PMID: 29986667603818710.1186/s12870-018-1360-z) ; Gaikpa DS, Koch S, Fromme FJ, Siekmann D, Würschum T, Miedaner T (2020) Genome-wide association mapping and genomic prediction of Fusarium head blight resistance, heading stage and plant height in winter rye (Secale cereale). Plant Breed 139:508–520. (PMID: 10.1111/pbr.12810) ; Gezan SA, Osorio LF, Verma S, Whitaker VM (2017) An experimental validation of genomic selection in octoploid strawberry. Hortic Res 4:16070. (PMID: 28090334522575010.1038/hortres.2016.70) ; Gianola D, Pérez-Enciso M, Toro MA (2003) On marker-assisted prediction of genetic value: beyond the ridge. Genetics 163:347–365. (PMID: 12586721146242510.1093/genetics/163.1.347) ; Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C (2010) Food security: the challenge of feeding 9 billion people. Science 327:812–818. (PMID: 2011046710.1126/science.1185383) ; González-Camacho JM, de Campos G, Pérez P, Gianola D, Cairns JE, Mahuku G, Babu R, Crossa J (2012) Genome-enabled prediction of genetic values using radial basis function neural networks. Theor Appl Genet 125:759–771. (PMID: 22566067340525710.1007/s00122-012-1868-9) ; González-Camacho JM, Crossa J, Pérez-Rodríguez P, Ornella L, Gianola D (2016) Genome-enabled prediction using probabilistic neural network classifiers. BMC Genom 17:208. (PMID: 10.1186/s12864-016-2553-1) ; González-Recio O, Forni S (2011) Genome-wide prediction of discrete traits using Bayesian regressions and machine learning. Genet Sel Evol 43:7. https://doi.org/10.1186/1297-9686-43-7. (PMID: 10.1186/1297-9686-43-7213295223400433) ; Gosal SS, Pathak D, Wani SH, Vij S, Pathak M (2020) Accelerated breeding of plants: methods and applications. In: Gosal S, Wani S (eds) Accelerated Plant Breeding, vol 1. Springer, Cham, pp 1–29. ; Granato I, Cuevas J, Luna-Vázquez F, Crossa J, Montesinos-López O, Burgueño J, Fritsche-Neto R (2018) BGGE: a new package for genomic-enabled prediction incorporating genotype × environment interaction models. G3 Genes Genomes Genet 8:3039–3047. (PMID: 10.1534/g3.118.200435) ; Gregory TR (2009) Artificial selection and domestication: modern lessons from Darwin’s enduring analogy. Evol Educ Outreach 2:5–27. (PMID: 10.1007/s12052-008-0114-z) ; Grover P, Kar AK, Dwivedi YK (2022) Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Ann Oper Res 308:177–213. (PMID: 10.1007/s10479-020-03683-9) ; Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinform 12:186. (PMID: 10.1186/1471-2105-12-186) ; Habier D, Fernando RL, Garrick DJ (2013) Genomic BLUP decoded: a look into the black box of genomic prediction. Genetics 194:597–607. (PMID: 23640517369796610.1534/genetics.113.152207) ; Habyarimana E, López-Cruz M, Baloch FS (2020) Genomic selection for optimum index with dry biomass yield, dry mass fraction of fresh material, and plant height in biomass sorghum. Genes 11:61. (PMID: 31948110701715510.3390/genes11010061) ; Hazra P, Hazra S, Acharya B, Dutta S, Saha S, Mahapatra P, Pradeepkumar P, Pal H, Chattopadhyay A, Chakraborty I (2022) Diversity of nutrient and nutraceutical contents in the fruits and its relationship to morphological traits in bitter gourd (Momordica charantia L). Sci Hort 305:111414. (PMID: 10.1016/j.scienta.2022.111414) ; He D, Kuhn D, Parida L (2016) Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction. Bioinform 32:i37–i43. (PMID: 10.1093/bioinformatics/btw249) ; Hernandez-Matheus A, Löschenbrand M, Berg K, Fuchs I, Aragüés-Peñalba M, Bullich-Massagué E, Sumper A (2022) A systematic review of machine learning techniques related to local energy communities. Renew Susta Energ Rev 170:112651. (PMID: 10.1016/j.rser.2022.112651) ; Holliday JA, Wang T, Aitken S (2012) Predicting adaptive phenotypes from multilocus genotypes in sitka spruce (Picea sitchensis) using random forest. G3 Genes Genomes Genet 2:1085–1093. (PMID: 10.1534/g3.112.002733) ; Jan HU, Abbadi A, Lücke S, Nichols RA, Snowdon RJ (2016) Genomic prediction of testcross performance in canola (Brassica napus). PLoS One 11:e0147769. (PMID: 26824924473266210.1371/journal.pone.0147769) ; Jannink JL, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177. (PMID: 2015698510.1093/bfgp/elq001) ; Jeong S, Kim JY, Kim N (2020) GMStool: GWAS-based marker selection tool for genomic prediction from genomic data. Sci Rep 10:19653. (PMID: 33184432766522710.1038/s41598-020-76759-y) ; Jewell C, Ng A (2019) Artificial intelligence: the new electricity. WIPO Mag 2019:2–6. ; Jia Y, Jannink JL (2012) Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics 192:1513–1522. (PMID: 23086217351215610.1534/genetics.112.144246) ; Jiang Y, Li C (2020) Convolutional neural networks for image-based high-throughput plant phenotyping: a review. Plant Phenomics 2020:1–22. (PMID: 10.34133/2020/4152816) ; Jiang Y, Reif JC (2015) Modeling epistasis in genomic selection. Genetics 201:759–768. (PMID: 26219298459668210.1534/genetics.115.177907) ; John M, Haselbeck F, Dass R, Malisi C, Ricca P, Dreischer C, Schultheiss SJ, Grimm DG (2022) A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species. Front Plant Sci 13:932512. (PMID: 36407627967347710.3389/fpls.2022.932512) ; Joshua SV, Priyadharson ASM, Kannadasan R (2022) Crop yield prediction using machine learning approaches on a wide spectrum. Comp Mater Cont 72(3):5663–5679. ; Khaki S, Pham H, Han Y, Khan AA, Lawanont W, Khan FA, Rehman AU, Ali MJ (2021) Deepcorn: a semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation. Knowl Based Syst 218:106874. (PMID: 10.1016/j.knosys.2021.106874) ; Kim SM, Reinke RF (2019) A novel resistance gene for bacterial blight in rice, Xa43 (t) identified by GWAS, confirmed by QTL mapping using a bi-parental population. PLoS ONE 14:e0211775. (PMID: 30753229637215710.1371/journal.pone.0211775) ; Koh JC, Spangenberg G, Kant S (2021) Automated machine learning for high-throughput image-based plant phenotyping. Remote Sens 13:858. (PMID: 10.3390/rs13050858) ; Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G (2021) Integrated genomic selection for rapid improvement of crops. Genomics 113(3):1070–1086. (PMID: 3361079710.1016/j.ygeno.2021.02.007) ; Kristensen PS, Jahoor A, Andersen JR (2019) Multi-trait and trait-assisted genomic prediction of winter wheat quality traits using advanced lines from four breeding cycles. Crop Breed Genet Genom 1:e1900010. ; Kulikov V, Kulikov A (2020) Regularization methods for the stable identification of probabilistic characteristics of stochastic structures. In: Kravets A, Bolshakov A, Shcherbakov M (eds) Cyber-physical systems advances in design & modelling. Studies in Systems, Decision and Control, vol 259. Springer, Cham, pp 179–191. ; Kuriakose SV, Pushker R, Hyde EM (2020) Data-driven decisions for accelerated plant breeding. In: Gosal S, Wani S (eds) Accelerated plant breeding, vol 1. Springer, Cham, pp 89–119. (PMID: 10.1007/978-3-030-41866-3_4) ; Lado B, Vázquez D, Quincke M (2018) Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L) baking quality. Theor Appl Genet 131:2719–2731. (PMID: 30232499624453510.1007/s00122-018-3186-3) ; Lee MA, Monteiro A, Barclay A, Marcar J, Miteva-Neagu M, Parker J (2020) A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques. Comp Elect Agric 168:105103. (PMID: 10.1016/j.compag.2019.105103) ; Legarra A, Ricard A, Filangi O (2016) GS3: genomic selection - Gibbs sampling - Gauss Seidel (and BayesCπ) (ANR project rules and tools). https://github.com/alegarra/gs3. ; Lenaerts B, Collard BC, Demont M (2019) Improving global food security through accelerated plant breeding. Plant Sci 287:110207. (PMID: 31481198674561910.1016/j.plantsci.2019.110207) ; Li Z, Yang L (2021) Weakly supervised deep reinforcement learning for video summarization with semantically meaningful reward. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, p 3239–3247. ; Liang Z, Gupta SK, Yeh CT, Zhang Y, Ngu DW, Kumar R, Patil HT, Mungra KD, Yadav DV, Rathore A, Srivastava RK, Gupta R, Yang J, Varshney RK (2018a) Phenotypic data from inbred parents can improve genomic prediction in pearl millet hybrids. G3 Genes Genomes Genet 8:2513–2522. (PMID: 10.1534/g3.118.200242) ; Liang Z, Gupta SK, Yeh CT, Zhang Y, Ngu DW, Kumar R, Patil HT, Mungra KD, Yadav DV, Rathore A, Srivastava RK (2018b) Phenotypic data from inbred parents can improve genomic prediction in pearl millet hybrids. G3 Genes Genome Genet 8:2513–2522. (PMID: 10.1534/g3.118.200242) ; Listgarten J, Weinstein M, Kleinstiver BP, Sousa AA, Joung JK, Crawford J, Fusi N (2018) Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs Nat. Biomed Eng 2:38–47. ; Liu X, Hongwu W, Hui W (2018) Factors affecting genomic selection revealed by empirical evidence in maize. Crop J 6:341–352. (PMID: 10.1016/j.cj.2018.03.005) ; Liu Y, Wang D, He F, Wang J, Joshi T, Xu D (2019) Phenotype prediction and genome-wide association study using deep convolutional neural network of soybean. Front Genet 10:1091. (PMID: 31824557688300510.3389/fgene.2019.01091) ; Long N, Gianola D, Rosa GJM, Weigel KA (2011) Application of support vector regression to genome-assisted prediction of quantitative traits. Theor Appl Genet 123:1065–1074. (PMID: 2173913710.1007/s00122-011-1648-y) ; Lyra DH, de Freitas-Mendonça L, Galli G, Alves FC, Granato ISC, Fritsche-Neto R (2017) Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids. Mol Breed 37:80. (PMID: 10.1007/s11032-017-0681-1) ; Ma J, Köster J, Qin Q, Hu S, Li W, Chen C, Liu XS, Li J, Cheng Q, Zhai J, Ma C (2016) CRISPR-DO for genome-wide CRISPR design and optimization. Bioinformatics 32(21):3336–3338. (PMID: 27402906609511910.1093/bioinformatics/btw476) ; Ma W, Qiu Z, Song J, Li J, Cheng Q, Zhai J, Ma C (2018) A deep convolutional neural network approach for predicting phenotypes from genotypes. Planta 248:1307–1318. (PMID: 3010139910.1007/s00425-018-2976-9) ; Ma X, Zhu K, Guan H, Feng J, Yu S, Liu G (2019) Calculation method for phenotypic traits based on the 3D reconstruction of maize canopies. Sensors 19(5):1201. (PMID: 30857269642759610.3390/s19051201) ; Majumdar SG, Rai A, Mishra DC (2019) R package GSelection: genomic selection. R package version 0.1. 0. https://cran.r-project.org/web/packages/GSelection/index.html. ; Martini JWR, Gao N, Cardoso DF, Wimmer V, Erbe M, Cantet RJC, Simianer H (2017) Genomic prediction with epistasis models: on the marker- coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE). BMC Bioinform 18:3. (PMID: 10.1186/s12859-016-1439-1) ; Matei G, Woyann LG, Milioli AS, Oliveira I, Zdziarski AD, Zanella R, Coelho ASG, Finatto T, Benin G (2018) Genomic selection in soybean: accuracy and time gain in relation to phenotypic selection. Mol Breed 38:1–13. (PMID: 10.1007/s11032-018-0872-4) ; Matias FI, Alves FC, Meireles KGX, Barrios SCL, do Valle CB, Endelman JB, Fritsche-Neto R (2019) On the accuracy of genomic prediction models considering multi-trait and allele dosage in Urochloa spp interspecific tetraploid hybrids. Mol Breed 39:100. (PMID: 10.1007/s11032-019-1002-7) ; Mellers G, Mackay I, Cowan S, Griffiths I, Martinez-Martin P, Pol JA, Bekele W, Tinker NA, Bentley AR, Howarth CJ (2020) Implementing within- cross genomic prediction to reduce oat breeding costs. Plant Genome 13:e20004. (PMID: 33016630863866110.1002/tpg2.20004) ; Merrick LF, Herr AW, Sandhu KS, Lozada DN, Carter AH (2022) Optimizing plant breeding programs for genomic selection. Agronomy 12:714. (PMID: 10.3390/agronomy12030714) ; Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829. (PMID: 11290733146158910.1093/genetics/157.4.1819) ; Mochida K, Koda S, Inoue K, Hirayama T, Tanaka S, Nishii R, Melgani F (2019) Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective. Gigascience 8(1):giy153. (PMID: 3052097510.1093/gigascience/giy153) ; Montesinos López OA, Montesinos López A, Crossa J (2022) Random forest for genomic prediction. In: Montesinos López OA, Montesinos López A, Crossa J (eds) Multivariate statistical machine learning methods for genomic prediction. Springer, Cham, pp 633–681. (PMID: 10.1007/978-3-030-89010-0_15) ; Montesinos-López OA, Martín-Vallejo J, Crossa J, Gianola D, Hernández-Suárez CM, Montesinos-López A, Juliana P, Singh R (2019a) New deep learning genomic- based prediction model for multiple traits with binary, ordinal, and continuous phenotypes. G3 Genes Genomes Genet 9:1545–1556. (PMID: 10.1534/g3.119.300585) ; Montesinos-López OA, Montesinos-López A, Luna- Vázquez FJ, Toledo FH, Pérez-Rodríguez P, Lillemo M, Crossa J (2019b) An R package for Bayesian analysis of multi-environment and multi-trait multi-environment data for genome-based prediction. Genes Genomes Genet 9:1355–1369. (PMID: 10.1534/g3.119.400126) ; Montesinos-López OA, Montesinos-López A, Tuberosa R, Maccaferri M, Sciara G, Ammar K, Crossa J (2019c) Multi-trait, multi-environment genomic prediction of durum wheat with genomic best linear unbiased predictor and deep learning methods. Front Plant Sci 10:1311. (PMID: 31787990685608710.3389/fpls.2019.01311) ; Morota G (2017) ShinyGPAS: interactive genomic prediction accuracy simulator based on deterministic formulas. Genet Sel Evol 49:91. (PMID: 29262775573885010.1186/s12711-017-0368-4) ; Nazarian A, Gezan SA (2016) GenoMatrix: a software package for pedigree-based and genomic prediction analyses on complex traits. J Hered 107:372–379. (PMID: 27025440488844210.1093/jhered/esw020) ; Neftci EO, Averbeck BB (2019) Reinforcement learning in artificial and biological systems. Nat Mach Intell 1:133–143. (PMID: 10.1038/s42256-019-0025-4) ; Nyine M, Uwimana B, Blavet N, Hřibová E, Vanrespaille H, Batte M, Akech V, Brown A, Lorenzen J, Swennen R, Doležel J (2018) Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana. Plant Genome 11:170090. (PMID: 10.3835/plantgenome2017.10.0090) ; Ogutu JO, Schulz-Streeck T, Piepho HP (2012) Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proc 6:S10. (PMID: 22640436336315210.1186/1753-6561-6-S2-S10) ; Ornella L, Pérez P, Tapia E, González-Camacho J, Burgueño J, Zhang X, Singh S, Vicente F, Bonnett D, Dreisigacker S, Singh R, Long N, Crossa J (2014) Genomic-enabled prediction with classification algorithms. Heredity (Edinb) 112:616–626. (PMID: 2442416310.1038/hdy.2013.144) ; Ortiz R, Gowda M, Zhao Y, Velazco JG, Van Eeuwijk FA, Jordan DR, Mace ES, Hunt CH, Malosetti M (2019) Genomic prediction of grain yield and drought- adaptation capacity in Sorghum is enhanced by multi-trait analysis. Front Plant Sci 10:997. (PMID: 10.3389/fpls.2019.00997) ; Pérez P, de los Campos G, Crossa J, Gianola D (2010) Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in r. Plant Genome 3:106–116. (PMID: 21566722309162310.3835/plantgenome2010.04.0005) ; Pérez-Enciso M, Zingaretti LM (2019) A guide for using deep learning for complex trait genomic prediction. Genes 10:553. (PMID: 31330861667820010.3390/genes10070553) ; Pérez-Enciso M, Ramírez-Ayala LC, Zingaretti LM (2020) SeqBreed: a python tool to evaluate genomic prediction in complex scenarios. Genet Sel Evol 52:1–9. (PMID: 10.1186/s12711-020-0530-2) ; Pichler M, Hartig F (2023) Machine learning and deep learning—a review for ecologists. Methods Ecol Evol 14:994–1016. (PMID: 10.1111/2041-210X.14061) ; Prakapenka D, Wang C, Liang Z, Bian C, Tan C, Da Y (2020) GVCHAP: a computing pipeline for genomic prediction and variance component estimation using haplotypes and SNP markers. Front Genet 11:282. (PMID: 32318093715412310.3389/fgene.2020.00282) ; Qiu Z, Cheng Q, Song J, Tang Y, Ma C (2016) Application of machine learning- based classification to genomic selection and performance improvement. In: Huang DS, Bevilacqua V, Premaratne P (eds) Intelligent computing theories and application. 12th International Conference, ICIC 2016, Lanzhou, China, August 2–5, 2016. Lecture Notes in Computer Science, vol 9771. Springer Cham, p 412–421. https://doi.org/10.1007/978-3-319-42291-6_41. ; Ramstein GP, Jensen SE, Buckler ES (2019) Breaking the curse of dimensionality to identify causal variants in Breeding 4. Theor Appl Genet 132:559–567. (PMID: 3054718510.1007/s00122-018-3267-3) ; Rauf S, Al-Khayri JM, Zaharieva M, Monneveux P, Khalil F (2016) Breeding strategies to enhance drought tolerance in crops. In: Al-Khayri JM, Jain SM, Johnson DV (eds) Advances in plant breeding strategies: agronomic, abiotic and biotic stress traits, vol 2. Springer, Cham, pp 397–445. (PMID: 10.1007/978-3-319-22518-0_11) ; Rice B, Lipka AE (2019) Evaluation of RR-BLUP genomic selection models that incorporate peak genome-wide association study signals in maize and sorghum. Plant Genome 12:180052. (PMID: 10.3835/plantgenome2018.07.0052) ; Rose MJ, Chithra NR (2023) Tree-based ensemble model prediction for hydrological drought in a tropical river basin of India. Int J Environ Sci Technol 20:4973–4990. (PMID: 10.1007/s13762-022-04208-6) ; Roth M, Muranty H, Di Guardo M, Guerra W, Patocchi A, Costa F (2020) Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple. Hortic Res 7:148. (PMID: 32922820745933810.1038/s41438-020-00370-5) ; Santangelo JS, Thompson KA, Johnson MT (2019) Herbivores and plant defences affect selection on plant reproductive traits more strongly than pollinators. J Evol Biol 32:4–18. (PMID: 3033930510.1111/jeb.13392) ; Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2:160. (PMID: 33778771798309110.1007/s42979-021-00592-x) ; Schnable PS, Springer NM (2013) Progress toward understanding heterosis in crop plants. Annu Rev Plant Biol 64:71–88. (PMID: 2339449910.1146/annurev-arplant-042110-103827) ; Schulthess AW, Wang Y, Miedaner T, Wilde P, Reif JC, Zhao Y (2016) Multiple- trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes. Theor Appl Genet 129:273–287. (PMID: 2656130610.1007/s00122-015-2626-6) ; Segelbacher G, Bosse M, Burger P, Godoy JA, Helsen P, Hvilsom C, Iacolina L, Kahric A, Manfrin C, Nonic M (2022) New developments in the field of genomic technologies and their relevance to conservation management. Conserv Genet 23:217–242. (PMID: 10.1007/s10592-021-01415-5) ; Shakoor N, Northrup D, Murray S, Monneveux P, Khalil F (2019) Big data driven agriculture: big data analytics in plant breeding, genomics, and the use of remote sensing technologies to advance crop productivity. Plant Phenome J 2:1–8. (PMID: 10.2135/tppj2018.12.0009) ; Shamshirgaran Y, Liu J, Sumer H, Verma PJ, Taheri-Ghahfarokhi A (2022) Tools for efficient genome editing; ZFN, TALEN, and CRISPR. In: Verma PJ, Sumer H, Liu J (eds) Applications of genome modulation and editing, vol 2495. NY: Springer. US, New York, pp 29–46. (PMID: 10.1007/978-1-0716-2301-5_2) ; Shi W, van de Zedde R, Jiang H, Kootstra G (2019) Plant-part segmentation using deep learning and multi-view vision. Biosys Eng 187:81–95. (PMID: 10.1016/j.biosystemseng.2019.08.014) ; Shirsat P (2020) Developing deep neural network for learner performance prediction in EKhool online learning platform. Multimed Res 3:24–31. (PMID: 10.46253/j.mr.v3i4.a3) ; Song L, Wang R, Yang X, Zhang A, Liu D (2023) Molecular markers and their applications in marker-assisted selection (MAS) in bread wheat (Triticum aestivum L). Agriculture 13:642. (PMID: 10.3390/agriculture13030642) ; Sousa TV, Caixeta ET, Alkimim ER, Oliveira ACB, Pereira AA, Sakiyama NS, Zambolim L, Resende MDV (2019) Early selection enabled by the implementation of genomic selection in Coffea arabica breeding. Front Plant Sci 9:1934. (PMID: 30671077633302410.3389/fpls.2018.01934) ; Srinivas K (2020) Prediction of e-learning efficiency by deep learning in E-khool online portal networks. Multimed Res 3:12–23. (PMID: 10.46253/j.mr.v3i4.a2) ; Stemmer M, Thumberger T, del Sol KM, Wittbrodt J, Mateo JL (2015) CCTop: an intuitive, flexible and reliable CRISPR/Cas9 target prediction tool. PLoS ONE 10:e0124633. (PMID: 25909470440922110.1371/journal.pone.0124633) ; Su G, Christensen OF, Ostersen T, Henryon M, Lund MS (2012) Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PLoS ONE 7:e45293. (PMID: 23028912344170310.1371/journal.pone.0045293) ; Tecle IY, Edwards JD, Menda N, Egesi C, Rabbi IY, Kulakow P, Kawuki R, Jannink JL, Mueller LA (2014) solGS: a web-based tool for genomic selection. BMC Bioinform 15:398. (PMID: 10.1186/s12859-014-0398-7) ; Teramoto S, Uga Y (2020) A deep learning-based phenotypic analysis of rice root distribution from field images. Plant Phenomics 2020:1–20. (PMID: 10.34133/2020/3194308) ; Thomson MJ, Zhao K, Wright M, McNally KL, Rey J, Tung CW, Reynolds A, Scheffler B, Eizenga G, McClung A, Kim H (2012) High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Mol Breed 29:875–886. (PMID: 10.1007/s11032-011-9663-x) ; Tong H, Nikoloski Z (2021) Machine learning approaches for crop improvement: leveraging phenotypic and genotypic big data. J Plant Physiol 257:153354. (PMID: 3338561910.1016/j.jplph.2020.153354) ; Tong H, Nankar AN, Liu J, Todorova V, Ganeva D, Grozeva S, Tringovska I, Pasev G, Radeva-Ivanova V, Gechev T, Kostova D (2022) Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits. Hort Res 9:072. ; Tsai HY, Janss LL, Andersen JR, Orabi J, Jensen JD, Jahoor A, Jensen J (2020) Genomic prediction and GWAS of yield, quality and disease-related traits in spring barley and winter wheat. Sci Rep 10:1–15. (PMID: 10.1038/s41598-020-63862-3) ; Tuberosa R (2012) Phenotyping for drought tolerance of crops in the genomics era. Front Physiol 3:347. https://doi.org/10.3389/fphys.2012.00347. (PMID: 10.3389/fphys.2012.00347230495103446691) ; Turner-Hissong SD, Bird KA, Lipka AE, King EG, Beissinger TM, Angelovici R (2020) Genomic prediction informed by biological processes expands our understanding of the genetic architecture underlying free amino acid traits in dry Arabidopsis seeds G3. Genes Genomes Genet 10:4227–4239. (PMID: 10.1534/g3.120.401240) ; Ubbens JR, Stavness I (2017) Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front Plant Sci 8:1190. (PMID: 28736569550063910.3389/fpls.2017.01190) ; Ukrainetz NK, Mansfield SD (2020) Assessing the sensitivities of genomic selection for growth and wood quality traits in lodgepole pine using Bayesian models. Tree Genet Genomes 16:1–19. (PMID: 10.1007/s11295-019-1404-z) ; Usai MG, Goddard ME, Hayes BJ (2009) LASSO with cross-validation for genomic selection. Genet Res (camb) 91:427–436. (PMID: 2012229810.1017/S0016672309990334) ; Vats S, Kumawat S, Kumar V, Patil GB, Joshi T, Sonah H, Sharma TR, Deshmukh R (2019) Genome editing in plants: exploration of technological advancements and challenges. Cells 8:1386. (PMID: 31689989691275710.3390/cells8111386) ; Verlouw JA, Clemens E, de Vries JH, Zolk O, Verkerk AJ, am Zehnhoff-Dinnesen A, Medina-Gomez C, Lanvers-Kaminsky C, Rivadeneira F, Langer T, van Meurs JB (2021) A comparison of genotyping arrays. Eur J Hum Genet. 29:1611–24. (PMID: 34140649856085810.1038/s41431-021-00917-7) ; Viana AP, de Resende MDV, Riaz S, Walker MA (2016) Genome selection in fruit breeding: application to table grapes. Sci Agric 73:142–149. (PMID: 10.1590/0103-9016-2014-0323) ; Vignal A, Milan D, SanCristobal M, Eggen A (2002) A review on SNPs and other types of molecular markers. Genet Sel Evol 34:275–305. (PMID: 12081799270544710.1186/1297-9686-34-3-275) ; Voss-Fels KP, Cooper M, Hayes BJ (2019) Accelerating crop genetic gains with genomic selection. Theor Appl Genet 132:669–686. (PMID: 3056936510.1007/s00122-018-3270-8) ; Wang C, Prakapenka D, Wang S, Eggen A (2014) GVCBLUP: a computer package for genomic prediction and variance component estimation of additive and dominance effects. BMC Bioinform 15:270. (PMID: 10.1186/1471-2105-15-270) ; Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z (2017) Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity (edinb) 118:302–310. (PMID: 2764961810.1038/hdy.2016.87) ; Wang H, Cimen E, Singh N, Buckler E (2020) Deep learning for plant genomics and crop improvement. Curr Opin Plant Biol 54:34–41. (PMID: 3198635410.1016/j.pbi.2019.12.010) ; Wolfe MD, Carpio DPD, Alabi O, Ezenwaka LC, Ikeogu UN, Kayondo IS, Lozano R, Okeke UG, Ozimati AA, Williams E, Egesi C, Kawuki RS, Kulakow P, Rabbi IY, Jannink JL (2017) Prospects for genomic selection in cassava breeding. Plant Genome 10:plantgenome2017.03.0015. https://doi.org/10.3835/plantgenome2017.03.0015. ; Wu C, Mozzoni LA, Moseley D, Hummer W, Ye H, Chen P, Shannon G, Nguyen H (2020) Genome-wide association mapping of flooding tolerance in soybean. Mol Breed 40:1–14. (PMID: 10.1007/s11032-019-1086-0) ; Xie C, Yang C (2020) A review on plant high-throughput phenotyping traits using UAV-based sensors. Comput Electron Agric 178:105731. (PMID: 10.1016/j.compag.2020.105731) ; Xie S, Shen B, Zhang C, Huang X, Zhang Y (2014) sgRNAcas9: a software package for designing CRISPR sgRNA and evaluating potential off-target cleavage sites. PLoS ONE 9:e100448. (PMID: 24956386406733510.1371/journal.pone.0100448) ; Xu S, Zhu D, Zhang Q (2014) Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc Natl Acad Sci USA 111:12456–12461. (PMID: 25114224415173210.1073/pnas.1413750111) ; Zhang Z, Sejdić E (2019) Radiological images and machine learning: trends, perspectives, and prospects. Comput Biol Med 108:354–370. (PMID: 31054502653136410.1016/j.compbiomed.2019.02.017) ; Zhang C, Marzougui A, Sankaran S (2020) High-resolution satellite imagery applications in crop phenotyping: an overview. Comput Electron Agric 175:105584. (PMID: 10.1016/j.compag.2020.105584) ; Zhang X, Sallam A, Gao L, Kantarski T, Pol J, DeHaan LR, Wyse DL, Anderson JA (2016) Establishment and optimization of genomic selection to accelerate the domestication and improvement of intermediate wheatgrass. Plant Genome 9:plantgenome2015.07.0059. https://doi.org/10.3835/plantgenome2015.07.0059. ; Zhao W, Lai X, Liu D, Zhang Z, Ma P, Wang Q, Zhang Z, Pan Y (2020) Applications of support vector machine in genomic prediction in pig and maize populations. Front Genet 11:598318. https://doi.org/10.3389/fgene.2020.598318. (PMID: 10.3389/fgene.2020.598318333436367744740)
- Contributed Indexing: Keywords: Artificial intelligence; Crop improvement; Deep learning; Machine learning; Modelling
- Entry Date(s): Date Created: 20240112 Date Completed: 20240411 Latest Revision: 20240411
- Update Code: 20240411
|
