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Unraveling Gene Regulation: Machine ...

Kvamme, Jarred M.,

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Unraveling Gene Regulation: Machine Learning Methods for Inferring Gene Regulatory Networks With Applications to Human Data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Unraveling Gene Regulation: Machine Learning Methods for Inferring Gene Regulatory Networks With Applications to Human Data // Jarred M Kvamme.
Author:	Kvamme, Jarred M.,
Description:	1 electronic resource (115 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=31848519
ISBN:	9798315751069

Unraveling Gene Regulation: Machine Learning Methods for Inferring Gene Regulatory Networks With Applications to Human Data /
Kvamme, Jarred M.,

Unraveling Gene Regulation: Machine Learning Methods for Inferring Gene Regulatory Networks With Applications to Human Data /Jarred M Kvamme. - 1 electronic resource (115 pages)

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

Understanding the molecular mechanisms controlling gene expression and how genetic variations shape phenotypic outcomes is a fundamental pursuit in biology. However, learning regulatory networks directly from genomic data remains a significant challenge. Causal network inference is a powerful tool for elucidating regulatory relationships among molecular phenotypes, such as gene expression or DNA methylation. I will first present results from studying how expression quantitative trait loci (eQTLs) regulate protein-coding genes and long non-coding RNAs in 48 different tissues and cell types from the Genotype-Tissue Expression Consortium.Next, I introduce Mendelian Randomization Genomic Network for trios (MRGNtrio), a novel causal inference framework which improves upon existing causal inference methods by: (i) inferring diverse regulatory relationships for a trio; (ii) accommodating the inclusion of many confounding variables; and (iii) eliminating the need for a large set of statistical dependence tests in inference. I demonstrate the effectiveness of MRGNtrio in learning causal biological networks through simulation and by analyzing eQTLs in human whole blood tissue.Lastly, I present a deep learning framework, Hierarchical Community Detection (DeepHCD), for community detection in gene regulatory networks. My approach aims to address the challenges of learning large networks by partitioning genes into functionally relevant communities which simplifies the network and enables existing causal methods to operate on smaller sets of identified gene modules. I validate the performance of DeepHCD in simulation against existing, commonly used approaches such as hierarhcial clustering and demonstrate its ability to enhance detection of fine community structure. Finally, I conclude with results from applying DeepHCD to group transcription factors associated with mouse hepatocyte differentiation, where I identify multiple transcription factor groups that act as regulatory units.

English

ISBN: 9798315751069Subjects--Topical Terms:

553671
Bioinformatics.
Subjects--Index Terms:

Causal network inference

Unraveling Gene Regulation: Machine Learning Methods for Inferring Gene Regulatory Networks With Applications to Human Data /
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520 $a Understanding the molecular mechanisms controlling gene expression and how genetic variations shape phenotypic outcomes is a fundamental pursuit in biology. However, learning regulatory networks directly from genomic data remains a significant challenge. Causal network inference is a powerful tool for elucidating regulatory relationships among molecular phenotypes, such as gene expression or DNA methylation. I will first present results from studying how expression quantitative trait loci (eQTLs) regulate protein-coding genes and long non-coding RNAs in 48 different tissues and cell types from the Genotype-Tissue Expression Consortium.Next, I introduce Mendelian Randomization Genomic Network for trios (MRGNtrio), a novel causal inference framework which improves upon existing causal inference methods by: (i) inferring diverse regulatory relationships for a trio; (ii) accommodating the inclusion of many confounding variables; and (iii) eliminating the need for a large set of statistical dependence tests in inference. I demonstrate the effectiveness of MRGNtrio in learning causal biological networks through simulation and by analyzing eQTLs in human whole blood tissue.Lastly, I present a deep learning framework, Hierarchical Community Detection (DeepHCD), for community detection in gene regulatory networks. My approach aims to address the challenges of learning large networks by partitioning genes into functionally relevant communities which simplifies the network and enables existing causal methods to operate on smaller sets of identified gene modules. I validate the performance of DeepHCD in simulation against existing, commonly used approaches such as hierarhcial clustering and demonstrate its ability to enhance detection of fine community structure. Finally, I conclude with results from applying DeepHCD to group transcription factors associated with mouse hepatocyte differentiation, where I identify multiple transcription factor groups that act as regulatory units.
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650 4 $a Genetics. $3 530508
650 4 $a Systematic biology. $3 3173492
650 4 $a Epidemiology. $3 568544
653 $a Causal network inference
653 $a Computational genomics
653 $a Deep learning
653 $a Genetic epidemiology
653 $a Gene regulatory networks
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710 2 $a University of Idaho. $b Bioinformatics & Computational Biology. $e degree granting institution. $3 3826219
720 1 $a Hohenlohe, Paul $e degree supervisor.
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