[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121714-en":3,"doc-seo-121714-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121714,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Predicting the Genotype-to-Phenotype Relationship in Plants Using Machine Learning and Deep Learning","Advances in computing and artificial intelligence have made genomic prediction and genomic selection feasible within minutes, supporting faster and more accurate phenotypic predictions for crop breeding. With dense genome-wide marker generation becoming more economical, this research evaluates computational models for predicting phenotypic values and selecting top-ranked samples for subsequent breeding cycles. The study compares approaches for genomic marker importance and analyzes predictive performance. Convolutional neural network architectures deliver the strongest overall predictions, while an entropy-based marker methodology improves marker selection and prediction accuracy.","Predicting the Genotype to Phenotype relationship in Plants using Machine Learning and Deep Learning  \nA thesis submitted to the College of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the degree of Master of Science in the Department of Computer Science University of Saskatchewan  \nSaskatoon  \nBy  \nKanav Kalra  \n©cKanav Kalra, January 2023. All rights reserved. Unless otherwise noted, copyright of the material in this thesis belongs to  \nthe author.  \nPermission to Use  \nIn presenting this thesis in partial fulfillment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis.  \nDisclaimer  \nReference in this thesis to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the University of Saskatchewan. The views and opinions of the author expressed herein do not state or reflect those of the University of Saskatchewan, and shall not be used for advertising or product endorsement purposes.  \nRequests for permission to copy or to make other uses of materials in this thesis in whole or part should be addressed to:  \nHead of the Department of Computer Science  \n176 Thorvaldson Building, 110 Science Place  \nUniversity of Saskatchewan  \nSaskatoon, Saskatchewan S7N 5C9 Canada  \nOR  \nDean  \nCollege of Graduate and Postdoctoral Studies  \nUniversity of Saskatchewan  \n116 Thorvaldson Building, 110 Science Place  \nSaskatoon, Saskatchewan S7N 5C9 Canada  \nAbstract  \nWith advancements in science and technology, it has become easier and faster to perform complex computations in a matter of a few minutes. Combined with the evolution in the area of artificial intelligence, machine learning has taken the world by storm. The availability of programming libraries with built-in machine learning methods has made it commonplace to use machine learning in order to solve tasks from all fields of science. The economical and computational feasibility to generate dense genome-wide markers has also grown because of advancements in marker and genotyping technology. An amalgamation of these has opened up the potential to improve the process of agricultural development. Indeed, the tasks of genomic prediction and genomic selection can be performed swiftly and offer the potential to reduce the cycle time in plant breeding by making accurate phenotypic predictions for the crops to be grown.  \nIn this study, we compare and evaluate the performance of computational models for the tasks of genomic prediction and genomic selection on four publicly available datasets of different species of agricultural crops. We examine and quantify the capability of various computational models to accurately predict the phenotypic values and determine the top-ranked samples to be used for the next breeding cycle. We also look at two methodologies to determine the important genomic markers, and compare their performance. We found that convolutional neural network models based on different architectures were able to make the best predictions and were capable of solving the tasks of genomic prediction and genomic selection. We also found that the entropy-based methodology performed well fo","cbCais64iSICaN5b","https://ap.wps.com/l/cbCais64iSICaN5b","pdf",11595504,1,106,"English","en",105,"# Abstract\n## Model comparison and evaluation\n## Genomic marker importance methods\n## Web application for prediction and marker assessment","[{\"question\":\"What tasks does the study focus on in plant breeding?\",\"answer\":\"The study focuses on genomic prediction and genomic selection, including predicting phenotypic values and determining top-ranked samples for the next breeding cycle.\"},{\"question\":\"Which modeling approach performs best for the prediction tasks?\",\"answer\":\"Convolutional neural network models with different architectures achieve the best prediction performance for genomic prediction and genomic selection.\"},{\"question\":\"How are important genomic markers identified and how does it affect accuracy?\",\"answer\":\"The study compares two methodologies for identifying important genomic markers. The entropy-based approach performs well and helps the computational models reach higher prediction accuracy.\"}]","Predicting the Genotype-to-Phenotype Relationship in Plants Using Machine Learning and Deep Learning | PDF",1785806440,267,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-the-genotype-to-phenotype-relationship-in-plants-using-machine-learning-and-deep-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-genotype-to-phenotype-relationship-in-plants-using-machine-learning-and-deep-learning/121714/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What tasks does the study focus on in plant breeding?","Question",{"text":75,"@type":76},"The study focuses on genomic prediction and genomic selection, including predicting phenotypic values and determining top-ranked samples for the next breeding cycle.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which modeling approach performs best for the prediction tasks?",{"text":80,"@type":76},"Convolutional neural network models with different architectures achieve the best prediction performance for genomic prediction and genomic selection.",{"name":82,"@type":73,"acceptedAnswer":83},"How are important genomic markers identified and how does it affect accuracy?",{"text":84,"@type":76},"The study compares two methodologies for identifying important genomic markers. 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