標(biāo)題: Titlebook: eQTL Analysis; Methods and Protocol Xinghua Mindy Shi Book 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020 mining.g [打印本頁(yè)] 作者: 貧血 時(shí)間: 2025-3-21 17:16
書目名稱eQTL Analysis影響因子(影響力)
書目名稱eQTL Analysis影響因子(影響力)學(xué)科排名
書目名稱eQTL Analysis網(wǎng)絡(luò)公開度
書目名稱eQTL Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱eQTL Analysis被引頻次
書目名稱eQTL Analysis被引頻次學(xué)科排名
書目名稱eQTL Analysis年度引用
書目名稱eQTL Analysis年度引用學(xué)科排名
書目名稱eQTL Analysis讀者反饋
書目名稱eQTL Analysis讀者反饋學(xué)科排名
作者: Latency 時(shí)間: 2025-3-21 20:36
Modes of the Tragic in Spanish Cinema detail with examples. Also, the commands to create publication-ready figures are presented. The last chapter concludes and discusses general topics related to the analysis of QTL and eQTL data in particular and genomic data in general.作者: 喚起 時(shí)間: 2025-3-22 04:09 作者: Creatinine-Test 時(shí)間: 2025-3-22 06:29 作者: 可卡 時(shí)間: 2025-3-22 11:08 作者: 廚房里面 時(shí)間: 2025-3-22 16:19
https://doi.org/10.1007/978-981-19-9107-3 which may produce confusion as to their respective applicability. In this chapter, we will introduce eQTLs, survey commonly used software to conduct a mapping study, as well as provide data correction methods to avoid the pitfalls of such analyses.作者: 廚房里面 時(shí)間: 2025-3-22 20:54
https://doi.org/10.1007/978-3-319-18902-4ction of eQTL that takes into account the combined effect of genetic variants within regulatory regions and leverages the idea that changes in gene expression often are the consequence of the alteration of transcription factor (TF) binding.作者: 輕彈 時(shí)間: 2025-3-22 23:14 作者: Substitution 時(shí)間: 2025-3-23 05:08
Modified Atmosphere Packaging of Foodces multi-omics data that have been used in many eQTL studies and integrative methodologies that incorporate multi-omics data for eQTL studies. Furthermore, we describe a statistical approach that can detect nonlinear causal relationships between eQTLs, called eQTL epistasis, and its importance.作者: Horizon 時(shí)間: 2025-3-23 08:23 作者: generic 時(shí)間: 2025-3-23 13:19
eQTL Mapping Using Transcription Factor Affinityction of eQTL that takes into account the combined effect of genetic variants within regulatory regions and leverages the idea that changes in gene expression often are the consequence of the alteration of transcription factor (TF) binding.作者: Biguanides 時(shí)間: 2025-3-23 16:02
Identification and Quantification of Splicing Quantitative Trait Locing variation, which presumably affects gene regulation. In this chapter, we outline the recent progress made and methods used to discover putative regulatory regions associated with complex traits. We will specifically focus on mapping splicing quantitative trait loci (sQTL) using Yoruba samples from GEUVADIS as a motivating example.作者: 我們的面粉 時(shí)間: 2025-3-23 18:44 作者: Inelasticity 時(shí)間: 2025-3-24 01:42
1064-3745 ation advice from the experts.This volume details state-of-art eQTL analysis, where interdisciplinary researchers are provided both theoretical and practical guidance to eQTL analysis and interpretation. Chapters guide readers through methods and tools for eQTL and QTL analysis and the usage of such作者: colony 時(shí)間: 2025-3-24 02:44
https://doi.org/10.1007/978-3-319-43458-2genetic model to develop genome-wide composite interval mapping (GCIM). This chapter covers the GCIM procedure in a backcross or doubled haploid populations. We describe the genetic model, parameter estimation, multi-locus genetic model, hypothesis tests, and software. Finally, some issues related to the GCIM method are discussed.作者: 明智的人 時(shí)間: 2025-3-24 08:34
Modified Cyclodextrins for Chiral Separationmorigenesis and development. Here we describe a detailed workflow for identifying eQTLs in cancer using existing packages and software. The key package is Matrix eQTL, which requires input data of genotypes, genes expression, and covariates. This pipeline can be easily applied in a related research field.作者: Ablation 時(shí)間: 2025-3-24 10:52 作者: Maximize 時(shí)間: 2025-3-24 15:37 作者: 改正 時(shí)間: 2025-3-24 19:08
https://doi.org/10.1007/978-3-319-10070-8 their connections to various latent factors, i.e., latent interactions among genes and environmental factors. In this chapter, we introduce a new scheme to harmoniously integrate mean and high-order effects of genetic variants on expression quantitative trait. We rigorously evaluate its validity and utility of signal augmentation.作者: 針葉 時(shí)間: 2025-3-25 01:07
https://doi.org/10.1007/1-4020-3794-5ion. More and more researchers working on eQTL analysis realize the importance of other types of QTLs beyond eQTL. In this chapter, we will explore some QTLs beyond eQTLs that show the regulatory association with eQTLs and explain the underlying link among these types of QTLs.作者: 微粒 時(shí)間: 2025-3-25 03:46 作者: AMPLE 時(shí)間: 2025-3-25 08:22 作者: CHASE 時(shí)間: 2025-3-25 14:13 作者: 蛤肉 時(shí)間: 2025-3-25 16:55 作者: Prostaglandins 時(shí)間: 2025-3-25 23:04
Genome-Wide Composite Interval Mapping (GCIM) of Expressional Quantitative Trait Loci in Backcross Pgenetic model to develop genome-wide composite interval mapping (GCIM). This chapter covers the GCIM procedure in a backcross or doubled haploid populations. We describe the genetic model, parameter estimation, multi-locus genetic model, hypothesis tests, and software. Finally, some issues related to the GCIM method are discussed.作者: CRUDE 時(shí)間: 2025-3-26 02:32
Expression Quantitative Trait Loci (eQTL) Analysis in Cancermorigenesis and development. Here we describe a detailed workflow for identifying eQTLs in cancer using existing packages and software. The key package is Matrix eQTL, which requires input data of genotypes, genes expression, and covariates. This pipeline can be easily applied in a related research field.作者: 裝入膠囊 時(shí)間: 2025-3-26 06:35 作者: urethritis 時(shí)間: 2025-3-26 10:38 作者: 子女 時(shí)間: 2025-3-26 13:04
Statistical and Machine Learning Methods for eQTL Analysist distinct computational and statistical challenges that require advanced methodological development to overcome. In recent years, many statistical and machine learning methods for eQTL analysis have been developed with the ability to provide a more complex perspective towards the identification of 作者: Aura231 時(shí)間: 2025-3-26 17:16
Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mapping. We perform extensive experiments on both simulated datasets and yeast datasets to demonstrate the effectiveness and efficiency of the proposed method. The results show that . can effectively detect both individual and group-wise signals and outperform the state-of-the-arts by a large margin. This 作者: 寒冷 時(shí)間: 2025-3-26 23:33 作者: 偶然 時(shí)間: 2025-3-27 01:20
Expression Quantitative Trait Loci Analysis in Multiple Tissuesy persist or vary in different tissues. When data are available on multiple tissues, it is often desired to borrow information across tissues and conduct an integrative analysis. Here we describe a multi-tissue eQTL analysis procedure, which improves the identification of different types of eQTL and facilitates the assessment of tissue specificity.作者: PHONE 時(shí)間: 2025-3-27 08:24 作者: 文字 時(shí)間: 2025-3-27 09:57 作者: nutrients 時(shí)間: 2025-3-27 15:29
Polymer Science and Technology Seriest distinct computational and statistical challenges that require advanced methodological development to overcome. In recent years, many statistical and machine learning methods for eQTL analysis have been developed with the ability to provide a more complex perspective towards the identification of 作者: 直覺沒有 時(shí)間: 2025-3-27 20:46 作者: 初次登臺(tái) 時(shí)間: 2025-3-27 22:13
Modified Inferior Turbinoplastye of reference genome sequence covered by similarly common CNVs in humans. It is this high level of variation that makes zebrafish interesting to investigate the effects of CNV on gene expression. Additionally, zebrafish share 70% of genetic similarities with humans, and 84% of genes associated with作者: micronutrients 時(shí)間: 2025-3-28 04:52 作者: 詩(shī)集 時(shí)間: 2025-3-28 06:19 作者: liposuction 時(shí)間: 2025-3-28 13:23 作者: EWER 時(shí)間: 2025-3-28 14:34
Identification and Quantification of Splicing Quantitative Trait Locimains one of the most important objectives of current biomedical research. Unlike Mendelian or familial diseases, which are usually caused by mutations in the coding regions of individual genes, complex diseases are thought to result from the cumulative effects of a large number of variants, of whic作者: affect 時(shí)間: 2025-3-28 21:50 作者: ADORE 時(shí)間: 2025-3-28 23:09
Combining eQTL and SNP Annotation Data to Identify Functional Noncoding SNPs in GWAS Trait-Associatedetected as trait-associated in a population-based genome-wide association study (GWAS). Our method’s key step is to combine, within a na?ve Bayes-like framework, three quantities for each SNP: (1) the .-value for the association test between the SNP’s genotype and the trait; (2) the .-value for the作者: BROTH 時(shí)間: 2025-3-29 05:01
Statistical and Machine Learning Methods for eQTL Analysiscing capabilities and better genotyping methods, we are now able to more fully appreciate how regulation of gene expression is consequential to one’s genotypes in coding and non-coding DNA. The identification of genetic loci that contribute to quantifiable variation in genetic expression is critical作者: ordain 時(shí)間: 2025-3-29 08:55
Sparse Regression Models for Unraveling Group and Individual Associations in eQTL Mappingarch interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biolo作者: FID 時(shí)間: 2025-3-29 13:36
Exploring Bayesian Approaches to eQTL Mapping Through Probabilistic Programming regression problem. An important aspect in the development of such models is the implementation of bespoke inference methodologies, a process which can become quite laborious, when multiple candidate models are being considered. We describe automatic, black-box inference in such models using ., a p作者: 廢墟 時(shí)間: 2025-3-29 19:08 作者: 玉米 時(shí)間: 2025-3-29 20:14 作者: 山崩 時(shí)間: 2025-3-30 00:32 作者: 手段 時(shí)間: 2025-3-30 07:33
Expression Quantitative Trait Loci (eQTL) Analysis in Cancerumor samples can provide an intermediate phenotype between genetic variation and complex traits to better understand how risk alleles contribute to tumorigenesis and development. Here we describe a detailed workflow for identifying eQTLs in cancer using existing packages and software. The key packag作者: 單調(diào)性 時(shí)間: 2025-3-30 10:09
QTL Analysis Beyond eQTLs also has potential impact for the study of transcription medicine for human complex disease. In the past two decades, the researchers focus on studying the eQTL, while more and more evidence shows that the regulatory genetic variants locating noncoding region have strong effect for the gene express作者: 使顯得不重要 時(shí)間: 2025-3-30 15:26
Quantitative Trait Loci (QTL) Mapping other and the environment. These are commonly identified through a statistical genetic analysis known as QTL mapping. Here, I present a step-by-step, practical approach to QTL mapping along with a sample data file. I focus on methods commonly used and discoveries that have been made in fishes, and 作者: 全能 時(shí)間: 2025-3-30 17:47
Expression Quantitative Trait Loci Analysis in Multiple Tissuesy persist or vary in different tissues. When data are available on multiple tissues, it is often desired to borrow information across tissues and conduct an integrative analysis. Here we describe a multi-tissue eQTL analysis procedure, which improves the identification of different types of eQTL and作者: 說不出 時(shí)間: 2025-3-30 23:55 作者: Cacophonous 時(shí)間: 2025-3-31 01:03
https://doi.org/10.1007/978-1-0716-0026-9mining; genetic variation; molecular phenotypes; expression; transcriptomic sequencing作者: BANAL 時(shí)間: 2025-3-31 08:19
https://doi.org/10.1007/978-981-19-9107-3given genotype. While the field of bioinformatics and genomics has experienced exponential growth with modern technological advances, an unintended consequence arises as a lack of a gold standard for many applications and methods, which may be compounded with ever-improving computational capabilitie作者: 斥責(zé) 時(shí)間: 2025-3-31 09:23 作者: hyperuricemia 時(shí)間: 2025-3-31 16:16 作者: 礦石 時(shí)間: 2025-3-31 18:10 作者: 倔強(qiáng)一點(diǎn) 時(shí)間: 2025-3-31 23:39
https://doi.org/10.1007/978-3-319-43458-2approaches have a low power in the detection of small-effect eQTL. To overcome this issue, we integrate polygenic background control with multi-locus genetic model to develop genome-wide composite interval mapping (GCIM). This chapter covers the GCIM procedure in a backcross or doubled haploid popul作者: Itinerant 時(shí)間: 2025-4-1 02:45
https://doi.org/10.1007/978-3-319-27886-5detected as trait-associated in a population-based genome-wide association study (GWAS). Our method’s key step is to combine, within a na?ve Bayes-like framework, three quantities for each SNP: (1) the .-value for the association test between the SNP’s genotype and the trait; (2) the .-value for the作者: dilute 時(shí)間: 2025-4-1 07:07 作者: nostrum 時(shí)間: 2025-4-1 14:08
Carsten Nieder,Luka Milas,K. Kian Angarch interest. Traditional eQTL methods focus on testing the associations between individual single-nucleotide polymorphisms (SNPs) and gene expression traits. A major drawback of this approach is that it cannot model the joint effect of a set of SNPs on a set of genes, which may correspond to biolo作者: 容易生皺紋 時(shí)間: 2025-4-1 14:46 作者: Vulvodynia 時(shí)間: 2025-4-1 18:47
