Author

Karansher Singh Sandhu

Washington State UniversityORCID 0000-0002-9395-2646Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetics· 2 Bioinformatics· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 2 Approach:deep learning· 2 Approach:machine learning (classical)· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:fine-mapping· 1 Research Focus:genome assembly· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Arron Hyrum CarteriD· 3 shared Michael PumphreyiD· 2 shared Achla SharmaiD· 1 shared Craig F. MorrisiD· 1 shared Dinesh Kumar SainiiD· 1 shared Jyoti ChaudharyiD· 1 shared Megan J. Lewien· 1 shared Meriem AouniD· 1 shared

Activity by Year

2021-2022 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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3 shared papersGenetics

Arron Hyrum CarterORCID

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4 papers

Recent·Most cited

BioinformaticsScientific ReportsPeer-reviewedAug 11, 2022

Consensus genomic regions associated with multiple abiotic stress tolerance in wheat and implications for wheat breeding

Mohammad Jafar Tanin, Dinesh Kumar Saini, Karansher Singh Sandhu…·Achla Sharma

The paper identifies consensus genomic regions associated with multiple abiotic stress tolerance in wheat and suggests their use in breeding for stress resilience.

95 citations

bulk RNA-seqstatistical model

𝕏 PostBluesky

GenomicsAgronomyPeer-reviewedDec 13, 2021

Genome-Wide Association Studies and Genomic Selection for Grain Protein Content Stability in a Nested Association Mapping Population of Spring Wheat

Karansher Singh Sandhu, Paul D. Mihalyov, Megan J. Lewien…·Arron Hyrum Carter

48 citationstop 4%

statistical modelGWAS / association mapping

𝕏 PostBluesky

GeneticsThe Plant GenomePeer-reviewedSep 5, 2021

Multi-Trait Machine and Deep Learning Models for Genomic Selection using Spectral Information in a Wheat Breeding Program

Karansher Singh Sandhu, Shruti Sunil Patil, Michael Pumphrey, Arron Hyrum Carter

This study uses multi-trait machine and deep learning models with spectral data to improve genomic selection predictions for grain yield and protein content in wheat.

154 citationstop 1%

plant (other)bulk RNA-seq

𝕏 PostBluesky

GeneticsBiologyPeer-reviewedJul 20, 2021

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Karansher Singh Sandhu, Meriem Aoun, Craig F. Morris, Arron Hyrum Carter

This study uses machine and deep learning models to predict end-use quality traits in wheat breeding, achieving higher accuracy than traditional methods.

63 citationstop 2%

machine learning (classical)deep learning

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