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Computational Methods For The Identification Of Multidomain Signatures of Disease States

Labilloy, Guillaume
http://orcid.org/0000-0002-9440-4211
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721230353227806

Abstract Details

2024, PhD, University of Cincinnati, Medicine: Biomedical Informatics.
The advent of sequencing technologies has revolutionized our understanding of disease. Researchers can now investigate the complex processes involved in the multi-layered transcription of genetic content, which regulates cell activity, homeostasis, and ultimately the organism's health. A disease can be conceived as a deviation from a homeostatic state, leading to cascading negative effects. A disease state, or more generally a disrupting factor (sometimes called a "perturbagen"), can be characterized by how it impacts the organism. This information constitutes its "signature", such as a list of differentially expressed genes or vectors of abundance of proteins or lipids. Significant efforts have focused on gathering these signatures into connectivity maps (CMAPs), which allow the identification of related disrupting factors based on the similarity of their signatures. CMAPs can overcome some limitations of traditional enrichment analysis. However, challenges remain. The integrative analysis of multi-domain data, as opposed to concurrent or sequential analysis, is still a challenge. The complexity of multi-omics analysis, involving retrieving datasets, annotations, and applying analytical pipelines, requires advanced programming skills, which can be a barrier for researchers without dedicated resources. Additionally, analysis pipelines need to scale up as assays become clinically available and more data is generated. To address these challenges, we developed machine learning tools to predict health outcomes, ranging from sepsis to dementia. Our goal is to build knowledge and expertise about integrative and extensible analytical pipelines for clinical, transcriptomics, and proteomics data. Specifically, we developed a statistical and machine learning model to classify patients by phenotype and predict mortality risk. We analyzed a prospective cohort of sepsis patients, selected predictive features, built and validated models, and then refined a robust model using only features available in the clinical environment. We also analyzed the post-synaptic protein-protein interactions with PSD95 (a key protein involved in neuronal signaling) in healthy subjects across four brain regions to establish a reference for future analyses of brain disorders. Furthermore, we evaluated the requirements and challenges of implementing an analytical tool at the bedside by integrating our sepsis phenotyping algorithm into a convenient docker container. In summary, the development and application of these machine learning tools provide systematic ways to combine multiple levels of complex clinical and/or multi-omics data. This translates into tangible and applicable analyses with potentially meaningful implications for healthcare.
Jaroslaw Meller, Ph.D. (Committee Chair)
Michal Kouril, Ph.D. (Committee Member)
Robert Smith, M.D. Ph.D. (Committee Member)
Faheem Guirgis, Ph.D M.A B.A. (Committee Member)
Michael Wagner, Ph.D. (Committee Member)
123 p.
Bioinformatics
data science; disease state; machine learning; multiomics; sepsis, brain disorders, alzheimer; proteomics, lipidomics, clinical data

Recommended Citations

Citations

  • Labilloy, G. (2024). Computational Methods For The Identification Of Multidomain Signatures of Disease States [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721230353227806

    APA Style (7th edition)

  • Labilloy, Guillaume. Computational Methods For The Identification Of Multidomain Signatures of Disease States. 2024. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721230353227806.

    MLA Style (8th edition)

  • Labilloy, Guillaume. "Computational Methods For The Identification Of Multidomain Signatures of Disease States." Doctoral dissertation, University of Cincinnati, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721230353227806

    Chicago Manual of Style (17th edition)

ucin1721230353227806
61
© 2024, some rights reserved.Creative Commons License Computational Methods For The Identification Of Multidomain Signatures of Disease States by Guillaume Labilloy is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.