ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2024
Efficient large-scale genomic prediction in approximate genome-based kernel model.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- The Role of Prior-Induced Regularization in Accuracy and Stability of Genomic Prediction Across Unimodal and Multimodal Models.Plants (Basel, Switzerland) · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
key messageThree computationally efficient algorithms of GP including RHBK, RHDK, and RHPK were developed in approximate genome-based kernel model. The drastically growing amount of genomic information contributes to increasing computational burden of genomic prediction (GP). In this study, we developed three computationally efficient algorithms of GP including RHBK, RHDK, and RHPK in approximate genome-based kernel model, which reduces dimension of genomic data via Nyström approximation and decreases the computational cost significantly thereby. According to the simulation study and real datasets, our three methods demonstrated predictive accuracy similar to or better than RHAPY, GBLUP, and rrBLUP in most cases. They also demonstrated a substantial reduction in computational time compared to GBLUP and rrBLUP in simulation. Due to their advanced computing efficiency, our three methods can be used in a wide range of application scenarios in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.