Milad Eskandari

University of Guelph

Canada

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Articles (9)

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

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Year:

2023

Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs

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Year:

2023

Soybean–SCN Battle: Novel Insight into Soybean’s Defense Strategies against Heterodera glycines

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Year:

2023

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

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Year:

2022

Effects of type I Diacylglycerol O-acyltransferase (DGAT1) genes on soybean (Glycine max L.) seed composition.

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Year:

2021

Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

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Year:

2021

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

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Year:

2021

Genomic regions associated with important seed quality traits in food-grade soybeans.

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Year:

2020

Genome-wide association identifies several QTLs controlling cysteine and methionine content in soybean seed including some promising candidate genes

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Year:

2020

Collaborators (6)

Sangeeta Dhaubhadel

Adjunct Professor

Western University

CANADA

Jennifer Geddes‐McAlister

CANADA

Dan Tulpan

Assistant Professor

University of Guelph

CANADA

Lewis Lukens

University of Guelph

CANADA

Istvan Rajcan

Professor

University of Guelph

CANADA

Mohsen Hesami

University of Guelph

CANADA

Milad Eskandari

Research Interests

Explore related searches

Contact this professor

Articles (9)

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs

Soybean–SCN Battle: Novel Insight into Soybean’s Defense Strategies against Heterodera glycines

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

Effects of type I Diacylglycerol O-acyltransferase (DGAT1) genes on soybean (Glycine max L.) seed composition.

Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean

Genomic regions associated with important seed quality traits in food-grade soybeans.

Genome-wide association identifies several QTLs controlling cysteine and methionine content in soybean seed including some promising candidate genes

Collaborators (6)

Sangeeta Dhaubhadel

Jennifer Geddes‐McAlister

Dan Tulpan

Lewis Lukens

Istvan Rajcan

Mohsen Hesami

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