4.5 Article

LightGBM: accelerated genomically designed crop breeding through ensemble learning

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Plant Sciences

Metabolomics-driven gene mining and genetic improvement of tolerance to salt-induced osmotic stress in maize

Xiaoyan Liang et al.

Summary: Research has shown that natural maize varieties display a large diversity of salt tolerance, yet the genetic variants underlying such diversity remain poorly discovered and applied. An LC-MS-based untargeted metabolomics approach identified 37 metabolite biomarkers related to SIOS tolerance, and 10 candidate genes significantly associated with SIOS tolerance and METO abundances. A citrate synthase, a glucosyltransferase, and a cytochrome P450 were validated to underlie genotype-METO-SIOS tolerance associations and improve the SIOS tolerance of elite maize inbred lines through additive effects.

NEW PHYTOLOGIST (2021)

Article Biotechnology & Applied Microbiology

LightGBM: accelerated genomically designed crop breeding through ensemble learning

Jun Yan et al.

Summary: LightGBM is an ensemble model of decision trees used for classification and regression prediction, showing superior performance in genomic selection-assisted breeding. Through benchmark tests, it demonstrates advantages in prediction precision, model stability, and computing efficiency.

GENOME BIOLOGY (2021)

Article Biotechnology & Applied Microbiology

The genetic mechanism of heterosis utilization in maize improvement

Yingjie Xiao et al.

Summary: The study demonstrates that although yield heterosis in maize hybrids is correlated with minor-effect epistatic QTLs, it may be the result of major-effect additive and dominant QTLs during early developmental stages. The transition to flowering is identified as a critical stage for heterosis formation, where epistatic QTLs are activated by paternal alleles counteracting deleterious maternal alleles. The proposed molecular breeding approach targets key genes to accelerate maize breeding by reducing deleterious epistatic interactions.

GENOME BIOLOGY (2021)

Review Agronomy

Genome optimization for improvement of maize breeding

Shuqin Jiang et al.

THEORETICAL AND APPLIED GENETICS (2020)

Article Biotechnology & Applied Microbiology

CUBIC: an atlas of genetic architecture promises directed maize improvement

Hai-Jun Liu et al.

GENOME BIOLOGY (2020)

Article Agriculture, Dairy & Animal Science

Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes

Rostam L. Abdollahi-Arpanahi et al.

GENETICS SELECTION EVOLUTION (2020)

Article Biotechnology & Applied Microbiology

One-step genome editing of elite crop germplasm during haploid induction

Timothy Kelliher et al.

NATURE BIOTECHNOLOGY (2019)

Review Biotechnology & Applied Microbiology

Breeding crops to feed 10 billion

Lee T. Hickey et al.

NATURE BIOTECHNOLOGY (2019)

Article Genetics & Heredity

Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits

Christina B. Azodi et al.

G3-GENES GENOMES GENETICS (2019)

Review Agronomy

State-of-the-art and novel developments of in vivo haploid technologies

Kamila Kalinowska et al.

THEORETICAL AND APPLIED GENETICS (2019)

Article Multidisciplinary Sciences

Genotype-by-environment interactions affecting heterosis in maize

Zhi Li et al.

PLOS ONE (2018)

Article Multidisciplinary Sciences

Genomic and environmental determinants and their interplay underlying phenotypic plasticity

Xin Li et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)

Article Plant Sciences

Speed breeding is a powerful tool to accelerate crop research and breeding

Amy Watson et al.

NATURE PLANTS (2018)

Review Plant Sciences

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives

Jose Crossa et al.

TRENDS IN PLANT SCIENCE (2017)

Article Multidisciplinary Sciences

Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models

Ping Zeng et al.

NATURE COMMUNICATIONS (2017)

Article Multidisciplinary Sciences

Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer

Giovanny Covarrubias-Pazaran

PLOS ONE (2016)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Agriculture, Dairy & Animal Science

Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits

Oscar Gonzalez-Recio et al.

LIVESTOCK SCIENCE (2014)

Article Biochemistry & Molecular Biology

Insights into the Maize Pan-Genome and Pan-Transcriptome

Candice N. Hirsch et al.

PLANT CELL (2014)

Review Plant Sciences

Machine learning for Big Data analytics in plants

Chuang Ma et al.

TRENDS IN PLANT SCIENCE (2014)

Review Plant Sciences

Genomic selection: genome-wide prediction in plant improvement

Zeratsion Abera Desta et al.

TRENDS IN PLANT SCIENCE (2014)

Article Multidisciplinary Sciences

Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights

Weiwei Wen et al.

NATURE COMMUNICATIONS (2014)

Review Plant Sciences

Will genomic selection be a practical method for plant breeding?

Akihiro Nakaya et al.

ANNALS OF BOTANY (2012)

Article Multidisciplinary Sciences

Brd1 Gene in Maize Encodes a Brassinosteroid C-6 Oxidase

Irina Makarevitch et al.

PLOS ONE (2012)

Article Genetics & Heredity

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

Paulino Perez-Rodriguez et al.

G3-GENES GENOMES GENETICS (2012)

Review Agriculture, Dairy & Animal Science

Reliable computing in estimation of variance components

I. Misztal

JOURNAL OF ANIMAL BREEDING AND GENETICS (2008)

Article Multidisciplinary Sciences

Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus m maize

Silvio Salvi et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2007)

Article Multidisciplinary Sciences

Loss of an MDR transporter in compact stalks of maize br2 and sorghum dw3 mutants

DS Multani et al.

SCIENCE (2003)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)