Hiroyoshi Iwata

Professor

University of Tokyo

Japan

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About

Hiroyoshi Iwata is a Professor at The University of Tokyo, Japan. His research focuses on quantitative genetics, machine learning applications in genomics, and optimization strategies for breeding in plant genetics. Recent articles highlight his work on genome-wide association studies, genomic prediction, and enhancing crop traits through innovative methodological approaches.

Articles (14)

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2025

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2025

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2023

Collaborators (7)

Kazutaka Kusunoki

JAPAN

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Sachiko Isobe

Professpr

Graduate School of Agricultural and Life Sciences, The University of Tokyo

JAPAN

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Kenta Shirasawa

Kazusa DNA Research Institute

JAPAN

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Hideki Takanashi

University of Tokyo

JAPAN

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Akio Kimura

Iwate University

JAPAN

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Kengo Sakurai

Assistant Professor

The Univeristy of Tokyo

JAPAN

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WEI GUO

Associate Professor

University of Tokyo

JAPAN

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Hiroyoshi Iwata

Research Interests

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About

Articles (14)

Optimization of crossing strategy based on the usefulness criterion in interpopulation crosses considering different marker effects among populations

A novel genome-wide association study method for detecting quantitative trait loci interacting with complex population structures in plant genetics

Machine learning for genomic and pedigree prediction in sugarcane

Genomic prediction and genome-wide association study using combined genotypic data from different genotyping systems: application to apple fruit quality traits

Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean

Quantitative Genetic Aspects of Accuracy of Tree Biomass Measurement Using LiDAR

Cross potential selection: a proposal for optimizing crossing combinations in recurrent selection using the usefulness criterion of future inbred lines

DeepCGP: A Deep Learning Method to Compress Genome-Wide Polymorphisms for Predicting Phenotype of Rice

Assessing the Potential for Genome-Assisted Breeding in Red Perilla Using Quantitative Trait Locus Analysis and Genomic Prediction

Genetic Dissection of Seasonal Changes in a Greening Plant Based on Time-Series Multispectral Imaging

Upcycling rice yield trial data using a weather-driven crop growth model

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data

DeepCGP: A Deep Learning Method to Compress Genome-Wide Polymorphisms for Predicting Phenotype of Rice

Bayesian optimisation for breeding schemes

Collaborators (7)

Kazutaka Kusunoki

Sachiko Isobe

Kenta Shirasawa

Hideki Takanashi

Akio Kimura

Kengo Sakurai

WEI GUO

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