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Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics./
Author:	Bhadani, Rahul Kumar.
Description:	1 online resource (76 pages)
Notes:	Source: Masters Abstracts International, Volume: 83-11.
Contained By:	Masters Abstracts International83-11.
Subject:	Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29209885click for full text (PQDT)
ISBN:	9798802723470

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics.
Bhadani, Rahul Kumar.

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics. - 1 online resource (76 pages)

Source: Masters Abstracts International, Volume: 83-11.

Thesis (M.S.)--The University of Arizona, 2022.

Includes bibliographical references

Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data has been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this thesis, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. Evaluation of our method on publicly available datasets demonstrates the superiority of our method scAGN in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew's correlation coefficient as well. Further, our method's runtime complexity is consistently faster compared to other methods.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798802723470Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

ClassificationIndex Terms--Genre/Form:

542853
Electronic books.

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics.
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500 $a Source: Masters Abstracts International, Volume: 83-11.
500 $a Advisor: An, Lingling.
502 $a Thesis (M.S.)--The University of Arizona, 2022.
504 $a Includes bibliographical references
520 $a Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data has been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this thesis, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. Evaluation of our method on publicly available datasets demonstrates the superiority of our method scAGN in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew's correlation coefficient as well. Further, our method's runtime complexity is consistently faster compared to other methods.
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538 $a Mode of access: World Wide Web
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650 4 $a Statistics. $3 517247
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653 $a Classification
653 $a Graph neural network
653 $a Machine learning
653 $a ScRNA-seq
653 $a Single-cell
653 $a Transductive learning
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