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A Machine Learning Framework for Accurate and Efficient Protein Function Prediction of Flavobacterium covae.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Machine Learning Framework for Accurate and Efficient Protein Function Prediction of Flavobacterium covae./
Author:	Rahman, Zaidur.
Description:	1 online resource (90 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-04.
Contained By:	Masters Abstracts International85-04.
Subject:	Industrial engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380541343

A Machine Learning Framework for Accurate and Efficient Protein Function Prediction of Flavobacterium covae.
Rahman, Zaidur.

A Machine Learning Framework for Accurate and Efficient Protein Function Prediction of Flavobacterium covae. - 1 online resource (90 pages)

Source: Masters Abstracts International, Volume: 85-04.

Thesis (M.S.)--North Dakota State University, 2023.

Includes bibliographical references

This thesis focuses on predicting protein functions in Flavobacterium covae, a Gramnegative bacterium causing columnaris disease mainly in channel catfish. It presents a multilabel classification challenge where each protein sequence can be associated with multiple Gene Ontology (GO) terms. Using a sophisticated blend of features in the form of homologous information, localization and essential genes properties derived from established databases and extracted physicochemical properties, a comprehensive picture of the protein landscape is painted. Three machine learning models are then used to analyze the relationships between these features and their GO terms. The models' performance is evaluated based on accuracy and compared to prediction results from models like PANNZER. This approach offers fresh insight into the bacterium's molecular biology, possibly facilitating new understanding of its pathogenicity. This could impact the management of columnaris disease, enhancing sustainability in the global fish farming industry and conserving aquatic biodiversity.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380541343Subjects--Topical Terms:

679492
Industrial engineering.
Subjects--Index Terms:

FeaturesIndex Terms--Genre/Form:

554714
Electronic books.

A Machine Learning Framework for Accurate and Efficient Protein Function Prediction of Flavobacterium covae.
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502 $a Thesis (M.S.)--North Dakota State University, 2023.
504 $a Includes bibliographical references
520 $a This thesis focuses on predicting protein functions in Flavobacterium covae, a Gramnegative bacterium causing columnaris disease mainly in channel catfish. It presents a multilabel classification challenge where each protein sequence can be associated with multiple Gene Ontology (GO) terms. Using a sophisticated blend of features in the form of homologous information, localization and essential genes properties derived from established databases and extracted physicochemical properties, a comprehensive picture of the protein landscape is painted. Three machine learning models are then used to analyze the relationships between these features and their GO terms. The models' performance is evaluated based on accuracy and compared to prediction results from models like PANNZER. This approach offers fresh insight into the bacterium's molecular biology, possibly facilitating new understanding of its pathogenicity. This could impact the management of columnaris disease, enhancing sustainability in the global fish farming industry and conserving aquatic biodiversity.
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538 $a Mode of access: World Wide Web
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650 4 $a Microbiology. $3 591510
650 4 $a Bioengineering. $3 598252
653 $a Features
653 $a Flavobacterium covae
653 $a Gene Ontology
653 $a Machine learning
653 $a Multi-label classification
653 $a Protein function prediction
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