找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Genomic Prediction of Complex Traits; Methods and Protocol Nourollah Ahmadi,Jér?me Bartholomé Book 2022 The Editor(s) (if applicable) and T

[復制鏈接]
查看: 39043|回復: 68
樓主
發(fā)表于 2025-3-21 19:17:52 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Genomic Prediction of Complex Traits
副標題Methods and Protocol
編輯Nourollah Ahmadi,Jér?me Bartholomé
視頻videohttp://file.papertrans.cn/383/382902/382902.mp4
概述Includes cutting-edge methods and protocols.Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice from the experts
叢書名稱Methods in Molecular Biology
圖書封面Titlebook: Genomic Prediction of Complex Traits; Methods and Protocol Nourollah Ahmadi,Jér?me Bartholomé Book 2022 The Editor(s) (if applicable) and T
描述.This volume explores the conceptual framework and the practical issues related to genomic prediction of complex traits in human medicine and in animal and plant breeding. The book is organized into five parts. Part One reminds molecular genetics approaches intending to predict phenotypic variations.? Part Two presents the principles of genomic prediction of complex traits, and reviews factors that affect its reliability. Part Three describes genomic prediction methods, including machine-learning approaches, accounting for different degree of biological complexity, and reviews the associated computer-packages. Part Four reports on emerging trends such as phenomic prediction and incorporation into genomic prediction models of “omics” data and crop growth models. Part Five is dedicated to lessons learned from cases studies in the fields of human health and animal and plant breeding, and to methods for analysis of the economic effectiveness of genomic prediction. Written in the highly successful .Methods in Molecular Biology. series format, the book provides theoretical bases and practical guidelines for an informed decision making of practitioners and identifies pertinent routes for
出版日期Book 2022
關鍵詞trait architecture; optimal marker density; prediction methods; phenotypic variations; genomic predictio
版次1
doihttps://doi.org/10.1007/978-1-0716-2205-6
isbn_softcover978-1-0716-2207-0
isbn_ebook978-1-0716-2205-6Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Busines
The information of publication is updating

書目名稱Genomic Prediction of Complex Traits影響因子(影響力)




書目名稱Genomic Prediction of Complex Traits影響因子(影響力)學科排名




書目名稱Genomic Prediction of Complex Traits網絡公開度




書目名稱Genomic Prediction of Complex Traits網絡公開度學科排名




書目名稱Genomic Prediction of Complex Traits被引頻次




書目名稱Genomic Prediction of Complex Traits被引頻次學科排名




書目名稱Genomic Prediction of Complex Traits年度引用




書目名稱Genomic Prediction of Complex Traits年度引用學科排名




書目名稱Genomic Prediction of Complex Traits讀者反饋




書目名稱Genomic Prediction of Complex Traits讀者反饋學科排名




單選投票, 共有 1 人參與投票
 

0票 0.00%

Perfect with Aesthetics

 

0票 0.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

1票 100.00%

Disdainful Garbage

您所在的用戶組沒有投票權限
沙發(fā)
發(fā)表于 2025-3-21 23:26:21 | 只看該作者
板凳
發(fā)表于 2025-3-22 04:12:29 | 只看該作者
Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools,sity SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its dense
地板
發(fā)表于 2025-3-22 07:21:44 | 只看該作者
5#
發(fā)表于 2025-3-22 09:48:25 | 只看該作者
,Genome and Environment?Based Prediction Models and Methods of Complex Traits Incorporating Genotypetion models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G?×?E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment
6#
發(fā)表于 2025-3-22 14:59:03 | 只看該作者
Accounting for Correlation Between Traits in Genomic Prediction,ian Ridge regression and best linear unbiased predictor, but also under a deep learning framework. The multitrait deep learning framework helps implement prediction models with mixed outcomes (continuous, binary, ordinal, and count, measured on different scales), which is not easy in conventional st
7#
發(fā)表于 2025-3-22 17:41:28 | 只看該作者
8#
發(fā)表于 2025-3-22 23:04:38 | 只看該作者
Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, the Dairy Cattlic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of “single-step” methodologies. Less emphasis in selection goals will be placed o
9#
發(fā)表于 2025-3-23 03:50:35 | 只看該作者
10#
發(fā)表于 2025-3-23 09:19:44 | 只看該作者
Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case,s of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associa
 關于派博傳思  派博傳思旗下網站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網 吾愛論文網 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經驗總結 SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網安備110108008328) GMT+8, 2025-10-14 12:06
Copyright © 2001-2015 派博傳思   京公網安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
本溪| 申扎县| 萝北县| 南涧| 定边县| 宝应县| 茌平县| 桃江县| 东阿县| 敦煌市| 冷水江市| 双桥区| 邹城市| 武夷山市| 光泽县| 成安县| 鄂尔多斯市| 仁布县| 临安市| 绥德县| 临颍县| 藁城市| 永定县| 得荣县| 湘西| 靖宇县| 和政县| 伊金霍洛旗| 高邮市| 阜南县| 休宁县| 蒙自县| 布尔津县| 临海市| 德清县| 武宁县| 瓦房店市| 聊城市| 金乡县| 康平县| 靖边县|