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Developing and Applying Machine Lear...

Choi, Jerome J.,

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Developing and Applying Machine Learning and Artificial Intelligence Approaches to Leverage Multi-Omics to Advance Understanding of Alzheimer's Disease Pathology /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing and Applying Machine Learning and Artificial Intelligence Approaches to Leverage Multi-Omics to Advance Understanding of Alzheimer's Disease Pathology // Jerome J Choi.
Author:	Choi, Jerome J.,
Description:	1 electronic resource (159 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Bioinformatics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32045365
ISBN:	9798315751137

Developing and Applying Machine Learning and Artificial Intelligence Approaches to Leverage Multi-Omics to Advance Understanding of Alzheimer's Disease Pathology /
Choi, Jerome J.,

Developing and Applying Machine Learning and Artificial Intelligence Approaches to Leverage Multi-Omics to Advance Understanding of Alzheimer's Disease Pathology /Jerome J Choi. - 1 electronic resource (159 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder driven by complex, multifactorial processes involving genetic, epigenetic, transcriptomic, and metabolic dysregulation. Understanding the molecular mechanisms underlying AD requires approaches that can capture the dynamic interactions across these biological layers. The disease is not the result of a single molecular event but emerges from disruptions in coordinated biological networks, making it difficult to identify clear causal pathways or biomarkers. Multi-omics technologies provide a means to interrogate these layers simultaneously, offering a comprehensive view of disease biology by linking genetic variation to downstream molecular and cellular phenotypes. However, the integration and interpretation of multi-omics data pose significant analytical challenges due to high dimensionality, heterogeneity, sparsity, and biological noise. Machine learning (ML) offers powerful tools to address these challenges by modeling non-linear relationships, uncovering hidden patterns, learning from high-dimensional data, and enabling accurate and interpretable phenotype prediction. This dissertation develops and applies novel ML-based frameworks for integrative multi-omics analysis in the context of AD. First, the roles of cooperative transcription factor (TF) pairs in regulating target genes within the gene regulatory networks (GRNs) of oligodendrocytes were characterized, utilizing multi-omics data and deep learning approaches. Second, COSIME: Cooperative Multi-view Integration and Scalable and Interpretable Model Explainer was developed with applications to Alzheimer's Disease (AD). Third, multi-omics integration of transcriptomics and metabolomics with machine learning was used to uncover novel risk factors for AD. By combining computational innovation with biological insight, this work advances systems-level understanding of AD and contributes new tools for precision medicine and biomarker discovery.

English

ISBN: 9798315751137Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Alzheimer's disease

Developing and Applying Machine Learning and Artificial Intelligence Approaches to Leverage Multi-Omics to Advance Understanding of Alzheimer's Disease Pathology /
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520 $a Alzheimer's disease (AD) is a progressive neurodegenerative disorder driven by complex, multifactorial processes involving genetic, epigenetic, transcriptomic, and metabolic dysregulation. Understanding the molecular mechanisms underlying AD requires approaches that can capture the dynamic interactions across these biological layers. The disease is not the result of a single molecular event but emerges from disruptions in coordinated biological networks, making it difficult to identify clear causal pathways or biomarkers. Multi-omics technologies provide a means to interrogate these layers simultaneously, offering a comprehensive view of disease biology by linking genetic variation to downstream molecular and cellular phenotypes. However, the integration and interpretation of multi-omics data pose significant analytical challenges due to high dimensionality, heterogeneity, sparsity, and biological noise. Machine learning (ML) offers powerful tools to address these challenges by modeling non-linear relationships, uncovering hidden patterns, learning from high-dimensional data, and enabling accurate and interpretable phenotype prediction. This dissertation develops and applies novel ML-based frameworks for integrative multi-omics analysis in the context of AD. First, the roles of cooperative transcription factor (TF) pairs in regulating target genes within the gene regulatory networks (GRNs) of oligodendrocytes were characterized, utilizing multi-omics data and deep learning approaches. Second, COSIME: Cooperative Multi-view Integration and Scalable and Interpretable Model Explainer was developed with applications to Alzheimer's Disease (AD). Third, multi-omics integration of transcriptomics and metabolomics with machine learning was used to uncover novel risk factors for AD. By combining computational innovation with biological insight, this work advances systems-level understanding of AD and contributes new tools for precision medicine and biomarker discovery.
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