Kengo Sakurai

Assistant Professor

The Univeristy of Tokyo

Japan

Research Interests

Explore related searches

Professors in Japan

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Kengo Sakurai is an Assistant Professor at The University of Tokyo, Japan. His research primarily focuses on optimization strategies in plant breeding and microbial interactions, utilizing advanced methodologies such as multi-omics data analysis and quantitative trait locus studies. Recent publications include works on genome-assisted breeding techniques and the application of time-series multispectral imaging in agricultural contexts.

Articles (7)

Year:

2025

Year:

2025

Year:

2024

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Collaborators (6)

Sachiko Isobe

Professpr

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

JAPAN

View details

Kenta Shirasawa

Kazusa DNA Research Institute

JAPAN

View details

Yuji Yamasaki

Assistant Professor

University of Tokyo

JAPAN

View details

Hideki Takanashi

University of Tokyo

JAPAN

View details

Shohei SHIMIZU

Shiga University

JAPAN

View details

Hiroyoshi Iwata

Professor

University of Tokyo

JAPAN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Kengo Sakurai

Research Interests

Explore related searches

Contact this professor

About

Articles (7)

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

I-SVVS: integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data

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

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

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

Time‐series multispectral imaging in soybean for improving biomass and genomic prediction accuracy

Chemical-Mediated Microbial Interactions Can Reduce the Effectiveness of Time-Series-Based Inference of Ecological Interaction Networks

Collaborators (6)

Sachiko Isobe

Kenta Shirasawa

Yuji Yamasaki

Hideki Takanashi

Shohei SHIMIZU

Hiroyoshi Iwata

Make your next move.