SPALL 发表于 2025-3-25 03:34:44

http://reply.papertrans.cn/19/1857/185621/185621_21.png

accrete 发表于 2025-3-25 10:28:07

,Vers un système de gestion de données, association, we examine how the integration of heterogeneous prior knowledge on the correlation structures between SNPs, and between genes can improve the robustness and the interpretability of eQTL mapping.

不妥协 发表于 2025-3-25 14:30:49

Robust Methods for Expression Quantitative Trait Loci Mapping association, we examine how the integration of heterogeneous prior knowledge on the correlation structures between SNPs, and between genes can improve the robustness and the interpretability of eQTL mapping.

Compass 发表于 2025-3-25 18:03:36

http://reply.papertrans.cn/19/1857/185621/185621_24.png

加剧 发表于 2025-3-26 00:02:27

https://doi.org/10.1007/2-287-31090-8ations as well as our contribution to the NP classification theory and algorithms. We also provide simulation examples and a genomic case study to demonstrate how to use the NP classification algorithm in practice.

SOB 发表于 2025-3-26 00:09:02

http://reply.papertrans.cn/19/1857/185621/185621_26.png

忧伤 发表于 2025-3-26 05:53:48

Genomic Applications of the Neyman–Pearson Classification Paradigmations as well as our contribution to the NP classification theory and algorithms. We also provide simulation examples and a genomic case study to demonstrate how to use the NP classification algorithm in practice.

冒烟 发表于 2025-3-26 11:30:44

http://reply.papertrans.cn/19/1857/185621/185621_28.png

cylinder 发表于 2025-3-26 15:58:55

http://reply.papertrans.cn/19/1857/185621/185621_29.png

沉默 发表于 2025-3-26 16:54:23

Book 2016hroughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace.  To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be su
页: 1 2 [3] 4 5 6
查看完整版本: Titlebook: Big Data Analytics in Genomics; Ka-Chun Wong Book 2016 Springer International Publishing Switzerland (Outside the USA) 2016 Big Data.Genom