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A Machine Learning Framework for Acc...

Rahman, Zaidur.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Machine Learning Framework for Accurate and Efficient Protein Function Prediction of Flavobacterium covae./
Author:	Rahman, Zaidur.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	90 p.
Notes:	Source: Masters Abstracts International, Volume: 85-04.
Contained By:	Masters Abstracts International85-04.
Subject:	Industrial engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30569296
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 90 p.

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

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

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.

ISBN: 9798380541343Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Features

A Machine Learning Framework for Accurate and Efficient Protein Function Prediction of Flavobacterium covae.
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100 1 $a Rahman, Zaidur. $3 3768435
245 1 2 $a A Machine Learning Framework for Accurate and Efficient Protein Function Prediction of Flavobacterium covae.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 90 p.
500 $a Source: Masters Abstracts International, Volume: 85-04.
500 $a Advisor: Narayanan, Lokesh Karthik.
502 $a Thesis (M.S.)--North Dakota State University, 2023.
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.
590 $a School code: 0157.
650 4 $a Industrial engineering. $3 526216
650 4 $a Microbiology. $3 536250
650 4 $a Bioengineering. $3 657580
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
690 $a 0546
690 $a 0202
690 $a 0410
710 2 $a North Dakota State University. $b Industrial and Manufacturing Engineering. $3 2102374
773 0 $t Masters Abstracts International $g 85-04.
790 $a 0157
791 $a M.S.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30569296