Author

Ye Han

ORCID 0000-0002-4200-1891Unclaimed ORCID profile · follow to get notified of new preprints

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Ye Han1 paper · University of Missouri

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Assay:imaging· 2 Research Focus:drug response / drug discovery· 1 Organism:plant (other)· 1 Approach:simulation· 1 Approach:statistical model· 1

Frequent Co-Authors

Hieu Pham· 3 shared Lizhi WangiD· 3 shared Saeed KhakiiD· 2 shared Wade Kent· 2 shared Andy Kuhl· 1 shared Arezoo Masoud· 1 shared Guiping HuiD· 1 shared Nima SafaeiiD· 1 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersBioinformatics

Hieu Pham

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3 papers

Recent·Most cited

High-Throughput Image-Based Plant Stand Count Estimation Using Convolutional Neural Networks

Saeed Khaki, Hieu Pham, Zahra Khalilzadeh, Arezoo Masoud, Nima Safaei, Ye Han, Wade Kent, Lizhi Wang

This paper introduces DeepStand, a deep learning method for high-throughput corn stand counting using images, outperforming existing methods.

PLoS ONE · Jul 28, 2022Peer-reviewedBioinformatics

DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation

Saeed Khaki, Hieu Pham, Ye Han, Andy Kuhl, Wade Kent, Lizhi Wang

DeepCorn uses semi-supervised deep learning to accurately count corn kernels in-field, improving yield estimation and data collection for agriculture.

Knowledge-Based Systems · Apr 1, 2021Peer-reviewedBioinformatics

A Look-ahead Monte Carlo Simulation Method for Improving Parental Selection in Trait Introgression

Saba Moeinizade, Ye Han, Hieu Pham, Guiping Hu, Lizhi Wang

The paper introduces a look-ahead Monte Carlo simulation method to improve parental selection in trait introgression, showing it achieves higher success probability than existing approaches in maize data.

Scientific Reports · Feb 16, 2021Peer-reviewedBioinformatics