This dissertation provides a deep dive into understanding gene expression, interaction, regulation, and the intricate mechanisms behind heliotropism and phototropism. Additionally, the research accentuates the significance of machine learning techniques, specifically for gene regulatory networks (GRNs).
Chapter 1 offers an exhaustive benchmarking of GRN methodologies, furthering our comprehension of machine-learning models relevant to GRNs. The evaluation revealed that GRNTE, SWING, and BiXGBoost emerged as top-performing methods in GRN inference. The suitability of these models varies depending on specific research criteria such as computational needs, dataset dimensions, and performance metric emphasis. An innovation of this chapter was the introduction of Colab workbooks and Rmarkdown documents, purposefully designed to democratize research accessibility by alleviating the need for coding expertise.
In Chapter 2, the molecular intricacies of heliotropism in helianthus are demystified. We identified pivotal genes and pathways underpinning heliotropism by harnessing the prowess of Differential Gene Expression, GRN, and enrichment analysis. A remarkable research point was gaining a deeper understanding of the diurnal variation in auxin-related genes, underlining their instrumental role in the plant's directional growth patterns. Furthermore, light perception, particularly the nuances of the red and far-red spectrum, combined with structural cell changes orchestrated by xyloglucan, are shown to be cardinal players in heliotropism's intricate dance.
Chapter 3 embarks on a journey into the realm of phototropism and its profound influence on stem growth. Our methodical gene expression analysis across diverse timeframes unearthed vital pathways governing this phenomenon. Growth-related, hormone-related, light-related, and, notably, immunity/defense-related pathways emerged as linchpins throughout the phototropism stages. These findings unravel an interconnected tapestry where a plant's immune response is woven intricately with its growth and light adaptation mechanisms.
This comprehensive research elucidates the myriad molecular mechanisms and patterns associated with heliotropism and phototropism. The insights gleaned hold potential for advancing the understanding of plant biology and for paving the way for innovative agricultural strategies leveraging these genetic revelations.
","abstract_html":"<p>This dissertation provides a deep dive into understanding gene expression, interaction, regulation, and the intricate mechanisms behind heliotropism and phototropism. Additionally, the research accentuates the significance of machine learning techniques, specifically for gene regulatory networks (GRNs).</p> <p>Chapter 1 offers an exhaustive benchmarking of GRN methodologies, furthering our comprehension of machine-learning models relevant to GRNs. The evaluation revealed that GRNTE, SWING, and BiXGBoost emerged as top-performing methods in GRN inference. The suitability of these models varies depending on specific research criteria such as computational needs, dataset dimensions, and performance metric emphasis. An innovation of this chapter was the introduction of Colab workbooks and Rmarkdown documents, purposefully designed to democratize research accessibility by alleviating the need for coding expertise.</p> <p>In Chapter 2, the molecular intricacies of heliotropism in helianthus are demystified. We identified pivotal genes and pathways underpinning heliotropism by harnessing the prowess of Differential Gene Expression, GRN, and enrichment analysis. A remarkable research point was gaining a deeper understanding of the diurnal variation in auxin-related genes, underlining their instrumental role in the plant's directional growth patterns. Furthermore, light perception, particularly the nuances of the red and far-red spectrum, combined with structural cell changes orchestrated by xyloglucan, are shown to be cardinal players in heliotropism's intricate dance.</p> <p>Chapter 3 embarks on a journey into the realm of phototropism and its profound influence on stem growth. Our methodical gene expression analysis across diverse timeframes unearthed vital pathways governing this phenomenon. Growth-related, hormone-related, light-related, and, notably, immunity/defense-related pathways emerged as linchpins throughout the phototropism stages. These findings unravel an interconnected tapestry where a plant's immune response is woven intricately with its growth and light adaptation mechanisms.</p> <p>This comprehensive research elucidates the myriad molecular mechanisms and patterns associated with heliotropism and phototropism. The insights gleaned hold potential for advancing the understanding of plant biology and for paving the way for innovative agricultural strategies leveraging these genetic revelations.</p>","abstract_has_math":false,"creators":["Nagori, Krupa"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Dr. Hagop Atamian","Dr. Mohamed Allali","Dr. Vincent Berardi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08-01T07:00:00Z","date_published":"2023-08-01T07:00:00Z","updated_at":"2026-07-24T01:38:31Z","subjects":["causal inference","deep learning","helianthus","data science","statistical analysis","Artificial Intelligence and Robotics","Computer Sciences","Numerical Analysis and Scientific Computing","Programming Languages and Compilers","Theory and Algorithms"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/40","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Hagop Atamian","Dr. Mohamed Allali","Dr. Vincent Berardi"]},{"key":"dc:creator","label":"Author","values":["Nagori, Krupa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-16T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["causal inference","deep learning","helianthus","data science","statistical analysis","Artificial Intelligence and Robotics","Computer Sciences","Numerical Analysis and Scientific Computing","Programming Languages and Compilers","Theory and Algorithms"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/40"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation provides a deep dive into understanding gene expression, interaction, regulation, and the intricate mechanisms behind heliotropism and phototropism. Additionally, the research accentuates the significance of machine learning techniques, specifically for gene regulatory networks (GRNs).
Chapter 1 offers an exhaustive benchmarking of GRN methodologies, furthering our comprehension of machine-learning models relevant to GRNs. The evaluation revealed that GRNTE, SWING, and BiXGBoost emerged as top-performing methods in GRN inference. The suitability of these models varies depending on specific research criteria such as computational needs, dataset dimensions, and performance metric emphasis. An innovation of this chapter was the introduction of Colab workbooks and Rmarkdown documents, purposefully designed to democratize research accessibility by alleviating the need for coding expertise.
In Chapter 2, the molecular intricacies of heliotropism in helianthus are demystified. We identified pivotal genes and pathways underpinning heliotropism by harnessing the prowess of Differential Gene Expression, GRN, and enrichment analysis. A remarkable research point was gaining a deeper understanding of the diurnal variation in auxin-related genes, underlining their instrumental role in the plant's directional growth patterns. Furthermore, light perception, particularly the nuances of the red and far-red spectrum, combined with structural cell changes orchestrated by xyloglucan, are shown to be cardinal players in heliotropism's intricate dance.
Chapter 3 embarks on a journey into the realm of phototropism and its profound influence on stem growth. Our methodical gene expression analysis across diverse timeframes unearthed vital pathways governing this phenomenon. Growth-related, hormone-related, light-related, and, notably, immunity/defense-related pathways emerged as linchpins throughout the phototropism stages. These findings unravel an interconnected tapestry where a plant's immune response is woven intricately with its growth and light adaptation mechanisms.
This comprehensive research elucidates the myriad molecular mechanisms and patterns associated with heliotropism and phototropism. The insights gleaned hold potential for advancing the understanding of plant biology and for paving the way for innovative agricultural strategies leveraging these genetic revelations.
"]},{"key":"dc:source","label":"Dc Source","values":["K. Nagori, \"Application of machine learning algorithms for elucidation of biological networks from time series gene expression data,\" Ph.D. dissertation, Chapman University, Orange, CA, 2023. https://doi.org/10.36837/chapman.000506"]},{"key":"dc:title","label":"Title","values":["Application of Machine Learning Algorithms for Elucidation of Biological Networks from Time Series Gene Expression Data"]}]}],"canonical_facts":{"dc:contributor":["Dr. Hagop Atamian","Dr. Mohamed Allali","Dr. Vincent Berardi"],"dc:creator":["Nagori, Krupa"],"dc:date.available":["2025-08-16T07:00:00Z"],"dc:description.abstract":["This dissertation provides a deep dive into understanding gene expression, interaction, regulation, and the intricate mechanisms behind heliotropism and phototropism. Additionally, the research accentuates the significance of machine learning techniques, specifically for gene regulatory networks (GRNs).
Chapter 1 offers an exhaustive benchmarking of GRN methodologies, furthering our comprehension of machine-learning models relevant to GRNs. The evaluation revealed that GRNTE, SWING, and BiXGBoost emerged as top-performing methods in GRN inference. The suitability of these models varies depending on specific research criteria such as computational needs, dataset dimensions, and performance metric emphasis. An innovation of this chapter was the introduction of Colab workbooks and Rmarkdown documents, purposefully designed to democratize research accessibility by alleviating the need for coding expertise.
In Chapter 2, the molecular intricacies of heliotropism in helianthus are demystified. We identified pivotal genes and pathways underpinning heliotropism by harnessing the prowess of Differential Gene Expression, GRN, and enrichment analysis. A remarkable research point was gaining a deeper understanding of the diurnal variation in auxin-related genes, underlining their instrumental role in the plant's directional growth patterns. Furthermore, light perception, particularly the nuances of the red and far-red spectrum, combined with structural cell changes orchestrated by xyloglucan, are shown to be cardinal players in heliotropism's intricate dance.
Chapter 3 embarks on a journey into the realm of phototropism and its profound influence on stem growth. Our methodical gene expression analysis across diverse timeframes unearthed vital pathways governing this phenomenon. Growth-related, hormone-related, light-related, and, notably, immunity/defense-related pathways emerged as linchpins throughout the phototropism stages. These findings unravel an interconnected tapestry where a plant's immune response is woven intricately with its growth and light adaptation mechanisms.
This comprehensive research elucidates the myriad molecular mechanisms and patterns associated with heliotropism and phototropism. The insights gleaned hold potential for advancing the understanding of plant biology and for paving the way for innovative agricultural strategies leveraging these genetic revelations.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/40"],"dc:source":["K. Nagori, \"Application of machine learning algorithms for elucidation of biological networks from time series gene expression data,\" Ph.D. dissertation, Chapman University, Orange, CA, 2023. https://doi.org/10.36837/chapman.000506"],"dc:subject":["causal inference","deep learning","helianthus","data science","statistical analysis","Artificial Intelligence and Robotics","Computer Sciences","Numerical Analysis and Scientific Computing","Programming Languages and Compilers","Theory and Algorithms"],"dc:title":["Application of Machine Learning Algorithms for Elucidation of Biological Networks from Time Series Gene Expression Data"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:31Z"}