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Deep Learning Gene Regulatory Networ...
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Maulding, Nathan,
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Deep Learning Gene Regulatory Network Dynamics From Transcriptomes /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Deep Learning Gene Regulatory Network Dynamics From Transcriptomes // Nathan Maulding.
作者:
Maulding, Nathan,
面頁冊數:
1 electronic resource (181 pages)
附註:
Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:
Dissertations Abstracts International86-02B.
標題:
Bioinformatics. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31329684
ISBN:
9798384010029
Deep Learning Gene Regulatory Network Dynamics From Transcriptomes /
Maulding, Nathan,
Deep Learning Gene Regulatory Network Dynamics From Transcriptomes /
Nathan Maulding. - 1 electronic resource (181 pages)
Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Regulation of gene expression is critical for all life processes. RNAseq allows for the investigation of the expression state and the inference of gene regulatory networks (GRNs). The GRN reveals mechanisms of disease and development both at an organismal and cellular level. Constructing GRNs from expression data is done by detecting statistical relationships over a series of samples. However, these relationships are noisy and do not take into account many important variables. To explore one such limitation, I implemented dual RNAseq to investigate the regulatory mechanisms that occur between both host and virus transcriptomes in SARS-CoV-2 infected samples (Chapter 3). To address the temporal aspect of GRNs, I developed a statistical framework for detecting relationships between transcription factors and their targets from inferred trajectory progressions ("pseudotime") in single-cells (Chapter 4). Finally, I developed an interpretable, time-aware deep learning transformer for modeling disease progression and demonstrate its ability to identify gene programs that promote breast cancer resistance post-treatment (Chapter 5).
English
ISBN: 9798384010029Subjects--Topical Terms:
553671
Bioinformatics.
Subjects--Index Terms:
Deep learning
Deep Learning Gene Regulatory Network Dynamics From Transcriptomes /
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Regulation of gene expression is critical for all life processes. RNAseq allows for the investigation of the expression state and the inference of gene regulatory networks (GRNs). The GRN reveals mechanisms of disease and development both at an organismal and cellular level. Constructing GRNs from expression data is done by detecting statistical relationships over a series of samples. However, these relationships are noisy and do not take into account many important variables. To explore one such limitation, I implemented dual RNAseq to investigate the regulatory mechanisms that occur between both host and virus transcriptomes in SARS-CoV-2 infected samples (Chapter 3). To address the temporal aspect of GRNs, I developed a statistical framework for detecting relationships between transcription factors and their targets from inferred trajectory progressions ("pseudotime") in single-cells (Chapter 4). Finally, I developed an interpretable, time-aware deep learning transformer for modeling disease progression and demonstrate its ability to identify gene programs that promote breast cancer resistance post-treatment (Chapter 5).
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