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Computational Methods for Gene Regul...

Jafari, Elham,

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Computational Methods for Gene Regulation Inference From Single-Cell Multi-Omics Data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational Methods for Gene Regulation Inference From Single-Cell Multi-Omics Data // Elham Jafari.
Author:	Jafari, Elham,
Description:	1 electronic resource (166 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332049
ISBN:	9798383574072

Computational Methods for Gene Regulation Inference From Single-Cell Multi-Omics Data /
Jafari, Elham,

Computational Methods for Gene Regulation Inference From Single-Cell Multi-Omics Data /Elham Jafari. - 1 electronic resource (166 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Gene Regulatory Networks (GRNs) map the complex interactions between transcription factors (TFs) and their target genes. GRN inference is essential for understanding cellular dynamics and biological functions. Despite progress, we can further improve current GRN inference methods. Recent advancements in cell profiling technologies enabled simultaneous measurements of gene expression and open chromatin across in thousands of cells. This research offers novel computational pipelines to integrate multi-omics single- cell gene expression and chromatin accessibility data for a multifaceted view of gene regulation as well as enhancing GRN inference through deep learning.Previously, most methods depended solely on gene expression data for GRN inference. This study overcomes such limitations by developing an innovative pipeline for unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency. Therefore, we enhance GRN inference by offering a more holistic view of gene regulation. Previous GRN inference solutions rely on various techniques including regression, correlation, information theory, and Ordinary Differential Equations (ODEs). According to system biology belief, gene regulation can be expressed as a system of ODEs, but existing ODE-based methodologies rely on oversimplified linear modeling. To surpass this, we introduce a non-linear neural ODE pipeline that models the dynamic rate of gene expression influenced by TFs in the regulatory network. Lastly, building upon the above holistic multi-omics integration data and non-linear ODE-based GRN inference model, we propose a hybrid GRN refinement strategy. Our approach is based on the biology of regulation involving TFs, genes, open chromatin, and known regulatory regions. We utilize multi-omics data to exclude TFs that are not associated with genes' regulatory regions to ensure biologically relevant regulatory interactions. Overall, my dissertation advances GRN inference research by employing powerful deep learning models over comprehensive multi-omics data, which significantly enhances our understanding of gene regulation.

English

ISBN: 9798383574072Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Deep learning

Computational Methods for Gene Regulation Inference From Single-Cell Multi-Omics Data /
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520 $a Gene Regulatory Networks (GRNs) map the complex interactions between transcription factors (TFs) and their target genes. GRN inference is essential for understanding cellular dynamics and biological functions. Despite progress, we can further improve current GRN inference methods. Recent advancements in cell profiling technologies enabled simultaneous measurements of gene expression and open chromatin across in thousands of cells. This research offers novel computational pipelines to integrate multi-omics single- cell gene expression and chromatin accessibility data for a multifaceted view of gene regulation as well as enhancing GRN inference through deep learning.Previously, most methods depended solely on gene expression data for GRN inference. This study overcomes such limitations by developing an innovative pipeline for unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency. Therefore, we enhance GRN inference by offering a more holistic view of gene regulation. Previous GRN inference solutions rely on various techniques including regression, correlation, information theory, and Ordinary Differential Equations (ODEs). According to system biology belief, gene regulation can be expressed as a system of ODEs, but existing ODE-based methodologies rely on oversimplified linear modeling. To surpass this, we introduce a non-linear neural ODE pipeline that models the dynamic rate of gene expression influenced by TFs in the regulatory network. Lastly, building upon the above holistic multi-omics integration data and non-linear ODE-based GRN inference model, we propose a hybrid GRN refinement strategy. Our approach is based on the biology of regulation involving TFs, genes, open chromatin, and known regulatory regions. We utilize multi-omics data to exclude TFs that are not associated with genes' regulatory regions to ensure biologically relevant regulatory interactions. Overall, my dissertation advances GRN inference research by employing powerful deep learning models over comprehensive multi-omics data, which significantly enhances our understanding of gene regulation.
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650 4 $a Systematic biology. $3 3173492
650 4 $a Bioinformatics. $3 553671
650 4 $a Genetics. $3 530508
653 $a Deep learning
653 $a Gene Regulatory Networks
653 $a Machine learning
653 $a Multi-omics integration
653 $a Single-cell gene expression
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