777 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Investigating the role of enhancer-mediated gene expression in the human brain and its potential contribution to psychiatric disorders
Autism spectrum disorder (ASD) and schizophrenia (SCZ) are two neuropsychiatric conditions with variable times of onset and are influenced by both genetic and environmental factors. Genome-wide association studies (GWASs) have led to the identification of numerous genetic loci common to both these disorders, however our understanding remains far from complete, with many clinical cases without a genetic cause. While increasing the statistical power of genome-wide association studies (GWASs) to find additional risk variants could rule-in or rule out rare cases of ASD and SCZ, this presently remains a difficult task. Furthermore, the biological functions for genetic susceptibility loci remains poorly understood, particularly for more-recent discoveries of loci devoid of gene bodies. On the other hand, recent biotechnological developments have made it possible to conduct high-resolution experimental measurements of the three-dimensional architecture of the genome, including enhancer-promoter interactions (EPIs). Such data have been used to connect GWAS risk variants to their potential target genes which, in turn, provide insights into underlying molecular mechanisms and cellular processes. The functions of enhancer-promoter interactions in controlling gene expression programmes is crucial to how implicated genes mediate neurological function and disease. Yet, knowledge on enhancer-promoter interactions remains to be used in conjunction with GWAS data, particularly on such data from specific brain cell types, which may be useful to uncover the biological underpinnings of psychiatric conditions. This thesis examines the role of enhancer-mediated gene expression in the human brain and its potential contribution to psychiatric conditions. In Chapter 2, I report on the identification of significant chromosomal interactions from studies of brain Hi-C data generated from neuronal and glial cells, with the goal to investigate the impact of EPIs genome-wide, as well as to provide a template for an in-depth understanding of how EPIs impact transcriptional regulation. In the Chapter 3, I discuss a novel approach integrating Activity by Contact (ABC) and gene set enrichment analyses of GWAS data in two steps. In the first step, ABC is used to predict enhancer-gene regulatory interactions in a given cell type (e.g., glial cells, neurons). Secondly, Hi-C coupled multi-marker analysis of genomic annotation (H-MAGMA) is used to assign the SNPs located in the regulatory regions identified by ABC to each gene and calculate gene-level association p-values. I applied this novel framework (ABC-HMAGMA) to GWAS data from SCZ and ASD, to identify novel SCZ and ASD trait-associated genes and molecular pathways. In Chapter 4, I have evaluated a potential novel mechanism for the regulation of enhancer activity within cells. I hypothesized that, in addition to its known roles in DNA replication and transcription, Topoisomerase I may regulate enhancer activity in brain cells. To test this hypothesis, I employed RNA-seq and transient transcriptome sequencing (TT-seq) data, a method that enriches for short-lived enhancer derived RNAs. These data showed that Topoisomerase I inhibition leads to significant changes in eRNA expression and offers evidence that such changes are relevant to the homeostatic functions for Top 1 in cellular gene expression regulation
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations
Artificial intelligence, particularly the subfield of machine learning, has
seen a paradigm shift towards data-driven models that learn from and adapt to
data. This has resulted in unprecedented advancements in various domains such
as natural language processing and computer vision, largely attributed to deep
learning, a special class of machine learning models. Deep learning arguably
surpasses traditional approaches by learning the relevant features from raw
data through a series of computational layers.
This thesis explores the theoretical foundations of deep learning by studying
the relationship between the architecture of these models and the inherent
structures found within the data they process. In particular, we ask What
drives the efficacy of deep learning algorithms and allows them to beat the
so-called curse of dimensionality-i.e. the difficulty of generally learning
functions in high dimensions due to the exponentially increasing need for data
points with increased dimensionality? Is it their ability to learn relevant
representations of the data by exploiting their structure? How do different
architectures exploit different data structures? In order to address these
questions, we push forward the idea that the structure of the data can be
effectively characterized by its invariances-i.e. aspects that are irrelevant
for the task at hand.
Our methodology takes an empirical approach to deep learning, combining
experimental studies with physics-inspired toy models. These simplified models
allow us to investigate and interpret the complex behaviors we observe in deep
learning systems, offering insights into their inner workings, with the
far-reaching goal of bridging the gap between theory and practice.Comment: PhD Thesis @ EPF
2023-2024 Boise State University Undergraduate Catalog
This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State
General Course Catalog [2022/23 academic year]
General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp
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