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An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States

Farouni, Tarek
http://orcid.org/0000-0002-3454-2946
http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539

Abstract Details

2017, Doctor of Philosophy, Ohio State University, Psychology.
The following dissertation consists of two parts. The first part presents an overview of latent variable models from a probabilistic perspective. The main goal of the overview is to give a birds-eye view of the topographic structure of the space of latent variable models in light of recent developments in statistics and machine learning that show how seemingly unrelated models and methods are in fact intimately related to each other. In the second part of the dissertation, we apply a Deep Latent Gaussian Model (DLGM) to high-dimensional, high-throughput functional epigenomics datasets with the goal of learning a latent representation of functional regions of the genome, both across DNA sequence and across cell-types. In the latter half of the dissertation, we first demonstrate that the trained generative model is able to learn a compressed two-dimensional latent representation of the data. We then show how the learned latent space is able to capture the most salient patterns of dependencies in the observations such that synthetic samples simulated from the latent manifold are able to reconstruct the same patterns of dependencies we observe in data samples. Lastly, we provide a biological interpretation of the learned latent manifold in terms of a continuous histone code and explain both the relevance and significance of the proposed generative approach to the problem of identifying functional regulatory regions from epigenomic marks.
Robert Cudeck (Advisor)
Ewy Mathé (Committee Member)
Zhong-Lin Lu (Committee Member)
Paul DeBoeck (Committee Member)
128 p.
Bioinformatics; Quantitative Psychology; Statistics
Probabilistic Latent Variable Models; Deep Generative Models; Deep Learning; Chromatin States; Histone Code; Epigenomics

Recommended Citations

Citations

  • Farouni, T. (2017). An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539

    APA Style (7th edition)

  • Farouni, Tarek. An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States. 2017. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539.

    MLA Style (8th edition)

  • Farouni, Tarek. "An Overview of Probabilistic Latent Variable Models with an Application to the Deep Unsupervised Learning of Chromatin States." Doctoral dissertation, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539

    Chicago Manual of Style (17th edition)

osu1492189894812539
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This open access ETD is published by The Ohio State University and OhioLINK.