標(biāo)題: Titlebook: Genomic Selection for Crop Improvement; New Molecular Breedi Rajeev K. Varshney,Manish Roorkiwal,Mark E. Sorrel Book 2017 Springer Internat [打印本頁] 作者: 棕櫚等 時(shí)間: 2025-3-21 18:27
書目名稱Genomic Selection for Crop Improvement影響因子(影響力)
書目名稱Genomic Selection for Crop Improvement影響因子(影響力)學(xué)科排名
書目名稱Genomic Selection for Crop Improvement網(wǎng)絡(luò)公開度
書目名稱Genomic Selection for Crop Improvement網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Genomic Selection for Crop Improvement被引頻次
書目名稱Genomic Selection for Crop Improvement被引頻次學(xué)科排名
書目名稱Genomic Selection for Crop Improvement年度引用
書目名稱Genomic Selection for Crop Improvement年度引用學(xué)科排名
書目名稱Genomic Selection for Crop Improvement讀者反饋
書目名稱Genomic Selection for Crop Improvement讀者反饋學(xué)科排名
作者: 罐里有戒指 時(shí)間: 2025-3-21 22:55
Training Population Design and Resource Allocation for Genomic Selection in Plant Breeding,rmation for selection to an indirect source of information through the training of genomic prediction models. While some features of phenotyping and field trials in plant breeding programs such as measurement of relevant phenotypes in relevant environments will remain the same, some features of plan作者: 充氣球 時(shí)間: 2025-3-22 02:15 作者: ineffectual 時(shí)間: 2025-3-22 05:46
Bayesian Genomic-Enabled Prediction Models for Ordinal and Count Data,or both models we provide details of their corresponding derivation and then apply them to a real data set. The proposed models were derived using a Bayesian framework. Bayesian logistic ordinal regression (BLOR) and Bayesian negative binomial regression (BNBR) make use of the Pólya-Gamma distributi作者: 退出可食用 時(shí)間: 2025-3-22 12:35
Genomic Selection for Small Grain Improvement,where there is a growing body of information. We also provide the reader with approaches to implementation of GS in applied breeding programs and how various scenarios affect gain from selection and cost relative to conventional breeding. Training population optimization is discussed as well as the 作者: 分貝 時(shí)間: 2025-3-22 14:30 作者: 分貝 時(shí)間: 2025-3-22 17:22
Genomic Selection in Hybrid Breeding,bining ability and heterotic groups are presented as a special feature of hybrid breeding, giving special attention to the breeding method of recurrent reciprocal selection. Subsequently, the cross-validated predictability is introduced as an evaluation criterion for the performance of GS and the re作者: 豪華 時(shí)間: 2025-3-22 21:29
Opportunities and Challenges to Implementing Genomic Selection in Clonally Propagated Crops,esirable deleterious effects, allows easy identification, propagation of favorable mutations, and is an efficient method for in vitro and ex vitro maintenance and conservation. However, these same characteristics pose challenges to genetic improvement. Many clonal fruits and forest trees have a long作者: 和平 時(shí)間: 2025-3-23 05:20
Status and Perspectives of Genomic Selection in Forest Tree Breeding,pendent on the time needed to complete a breeding generation. Additionally, the uncertainties associated with conducting decade-long breeding programs can be high. The convergence of genomics and quantitative genetics has now established the paradigm of genomic selection as a way to accelerate breed作者: Juvenile 時(shí)間: 2025-3-23 09:22
Book 2017 useful review ?explains germplasm use, phenotyping evaluation, marker genotyping methods, and statistical models involved in genomic selection. It also includes examples of ongoing activities of genomic selection for crop improvement and efforts initiated to deploy the genomic selection in some imp作者: ineptitude 時(shí)間: 2025-3-23 10:00
fforts initiated to deploy the genomic selection in some important crops. In order to understand the potential of GS breeding, it is high time to bring complete information in the form of a book that can serve as a ready reference for geneticist and plant breeders..978-3-319-87489-0978-3-319-63170-7作者: 種類 時(shí)間: 2025-3-23 16:06
https://doi.org/10.1007/978-1-4615-9656-1illustrate the proposed models using simulation and a real data set. Results indicate that our models for ordinal categorical and count data are a good alternative for analyzing ordinal and count data in the context of genomic-enabled prediction.作者: promote 時(shí)間: 2025-3-23 20:39 作者: 不可侵犯 時(shí)間: 2025-3-23 23:48 作者: conscribe 時(shí)間: 2025-3-24 05:13
Book 2017ortant crops. In order to understand the potential of GS breeding, it is high time to bring complete information in the form of a book that can serve as a ready reference for geneticist and plant breeders..作者: aplomb 時(shí)間: 2025-3-24 06:34
Current Topics in Veterinary Medicinesed in the derivation process is the key element for GS. Assuming a typical dataset for GS, construction of a prediction model using a linear model and determination of the model parameters using the Bayesian estimation with Gibbs sampling are explained. In addition, a sample output from the implemented software is presented.作者: 打包 時(shí)間: 2025-3-24 14:08 作者: 經(jīng)典 時(shí)間: 2025-3-24 16:29
mportant crops.Serves as a handbook, providing basic as well.Genomic Selection for Crop Improvement.?serves as handbook for users by providing basic as well as advanced understandings of genomic selection. This useful review ?explains germplasm use, phenotyping evaluation, marker genotyping methods,作者: wangle 時(shí)間: 2025-3-24 19:16 作者: ESPY 時(shí)間: 2025-3-25 01:43 作者: amenity 時(shí)間: 2025-3-25 05:07
Training Population Design and Resource Allocation for Genomic Selection in Plant Breeding,esign is the genetic relationship with the target population, making the definition of the target population the first and most important step in genomic selection model development. Several algorithms for training population design that show promise have been published and should be considered by p作者: 我不明白 時(shí)間: 2025-3-25 07:38 作者: Meander 時(shí)間: 2025-3-25 13:52 作者: DNR215 時(shí)間: 2025-3-25 17:56 作者: 緯度 時(shí)間: 2025-3-25 23:40 作者: inculpate 時(shí)間: 2025-3-26 02:01 作者: 戰(zhàn)勝 時(shí)間: 2025-3-26 07:36 作者: 煩憂 時(shí)間: 2025-3-26 11:05
Diagnosis of human peroxisomal disorders(GS) has potential to capture small and large effect genetic factors and deal with the complex traits. Over the last decade, large scale genomic resources have been developed in majority of the legume crops, which provide a perfect platform to deploy genome-wide information in selecting breeding mat作者: 連累 時(shí)間: 2025-3-26 16:14 作者: 完成 時(shí)間: 2025-3-26 18:21
Nitrogen Diagnosis and Decision Supportof several segregating alleles, overdominance and epistatic interactions, at each locus of highly heterozygous clonal crop decreases efficiency of phenotypic selection in breeding programs and genetic studies. Genomic selection (GS) that uses genome-wide genotypic data to predict the phenotypic perf作者: 不發(fā)音 時(shí)間: 2025-3-27 00:16 作者: 碎片 時(shí)間: 2025-3-27 05:03 作者: Fatten 時(shí)間: 2025-3-27 08:28
978-3-319-87489-0Springer International Publishing AG 2017作者: aspect 時(shí)間: 2025-3-27 10:38
https://doi.org/10.1007/978-3-319-63170-7Crop improvement; Genomic Selection; Marker genotyping; Phenotyping evaluation; Statistical models; Syste作者: jovial 時(shí)間: 2025-3-27 13:59 作者: SKIFF 時(shí)間: 2025-3-27 21:32
https://doi.org/10.1007/978-981-99-5624-1rmation for selection to an indirect source of information through the training of genomic prediction models. While some features of phenotyping and field trials in plant breeding programs such as measurement of relevant phenotypes in relevant environments will remain the same, some features of plan作者: Postmenopause 時(shí)間: 2025-3-27 23:35
Current Topics in Veterinary Medicinecess used to identify valuable individuals for generating descendants in the breeding process. To obtain the estimated values of a trait, a prediction model that describes the relationship between the explanatory factors observed in the samples and the trait values is derived using a dataset consist作者: TAP 時(shí)間: 2025-3-28 03:22 作者: circuit 時(shí)間: 2025-3-28 07:36
https://doi.org/10.1007/978-1-4939-2575-9where there is a growing body of information. We also provide the reader with approaches to implementation of GS in applied breeding programs and how various scenarios affect gain from selection and cost relative to conventional breeding. Training population optimization is discussed as well as the 作者: 憤世嫉俗者 時(shí)間: 2025-3-28 11:57 作者: 影響 時(shí)間: 2025-3-28 17:34 作者: Physiatrist 時(shí)間: 2025-3-28 19:25 作者: Outspoken 時(shí)間: 2025-3-29 01:51
https://doi.org/10.1007/978-3-030-78663-2pendent on the time needed to complete a breeding generation. Additionally, the uncertainties associated with conducting decade-long breeding programs can be high. The convergence of genomics and quantitative genetics has now established the paradigm of genomic selection as a way to accelerate breed作者: CURL 時(shí)間: 2025-3-29 04:17
