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Identifying Biologically Relevant Mechanisms And Biomarkers Using Novel Bioinformatics Methods, Samer Hanoudi

Wayne State University Dissertations

There is a tremendous need to analyze molecular and patient clinical data to identify biomarkers, biological mechanisms, or to simply classify samples accurately. Issues such as: i) limited tools to diagnose many diseases, ii) not considering biological interactions, or iii) damaged DNA samples could cause a challenge in identifying valuable insights. In this work, I try to address these issues by developing different bioinformatic frameworks.First, I present three frameworks to identify i) Sarcoidosis biomarkers, ii) Tuberculosis biomarkers and iii) Cystic fibrosis (CF) biomarkers. I identified Sarcoidosis biomarkers I applied them to classify Sarcoidosis samples from non-Sarcoidosis (healthy controls, Tuberculosis, and …

Representation Learning With Autoencoders For Electronic Health Records, Najibesadat Sadatijafarkalaei

Wayne State University Theses

Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however

is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this research, we propose a predictive modeling approach based on deep feature representations and word embedding techniques. Our method uses different deep …

Methods To Integrate Genetic And Clinical Data For Disease Subtyping, Diana Mabel Diaz-Herrera

Wayne State University Dissertations

Enormous efforts have been made to collect genetic and clinical data from cancer patients to advance the understanding of disease development and progression. Processing and analyzing these flows of data is challenging. Many computational methods have been proposed to help different fronts of biology and medicine. The integration of clinical and genetic data using computational methods towards personalized medicine is considered the future for oncology studies, and this thesis contributes in this direction. This thesis presents three new data integration approaches to elucidate granular and meaningful disease sub-types from high-dimensional complex genetic and clinical variables, which is an essential step …

Text Mining Of Variant-Genotype-Phenotype Associations From Biomedical Literature, Nafiseh Saberian

Wayne State University Theses

In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this information is a challenging task due to i) the complexity of natural language processing, ii) inconsistent use of standard recommendations for variant description, and iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant-gene-disease associations from …

Biological And Computational Studies Of The Structure And Function Of Pul103, A Human Cytomegalovirus Tegument Protein, Ashley N. Anderson

Wayne State University Dissertations

Human cytomegalovirus (HCMV) is an enveloped, single segment, double-stranded DNA virus. HCMV infection causes disease in immunocompromised (HIV patients, transplant recipients) and immunodeficient (fetuses, neonates) populations. Current treatments are effective but are either limited in use or can lead to organ damage and/or antiviral resistance, and no vaccines are available. Additional antiviral targets are needed. HCMV pUL103 is a potential antiviral target. pUL103 is a conserved herpesvirus protein present in the tegument, layer of proteins and RNA between the envelope and capsid of HCMV virions. pUL103 helps reorganize cellular secretory machinery (Golgi, endosomes) to form the cytoplasmic virion assembly compartment …

Missing Heritability And Novel Germline Risk Loci In Hereditary Ovarian Cancer: Insights From Whole Exome Sequencing And Functional Analyses, Jaime Lyn Stafford

Wayne State University Dissertations

While 25% of ovarian cancer (OVCA) cases are due to inherited factors, most of the genetic risk remains unexplained. This study addressed this gap by identifying previously undescribed OVCA risk loci through the whole exome sequencing (WES) of 48 BRCA1/BRCA2 wild type women diagnosed with OVCA, selected for high risk of genetic inheritance. Five clearly pathogenic variants were identified in this sample, four of which are in two genes featured on current multi-gene panels; (RAD51D, ATM). In addition, a high impact variant in FANCM (R1931*) was identified. FANCM has been recently implicated in familial breast cancer risk but is not …

Qualitative Change Detection Approach For Preventive Therapies, Cristina Mitrea

Wayne State University Dissertations

Currently, most diseases are diagnosed only after disease-associated changes have occurred. In this PhD dissertation, we propose a paradigm shift from treating the disease to maintaining the healthy state. The proposed approach is able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility of therapeutic interventions before the occurrence of symptoms. The change detection method exploits knowledge from biological networks and longitudinal data using a system impact analysis approach. This approach is validated on eight datasets, for seven different model organisms and eight biological phenomena. On these data, our proposed method performs well, consistently identifying …

Integration Of Mutation And Gene Expression Data To Identify Disease Subtypes, Sahar Ansari

Wayne State University Theses

Understanding the biological insights hidden in the vast amount of data collected, while investigating a disease, is the main goal for collecting such data in the first place.

Changes in the gene expression or the function of proteins are important components in progression of a disease and is a key to understanding the disease mechanism.

However, more often than not, the causes of such changes are not easily identified. In many cases, genetic variants may cause some of the observed gene expression changes.

In this thesis, we focus on identifying the variants that significantly alter gene expression for an individual …

Functional Analysis Of Sin3 Isoforms In Drosophila, Nirmalya Saha

Wayne State University Dissertations

he multisubunit SIN3 complex is a global transcriptional regulator. In Drosophila, a single Sin3A gene encodes different isoforms of SIN3, of which SIN3 187 and SIN3 220 are the major isoforms. Previous studies have demonstrated functional non-redundancy of SIN3 isoforms. The role of SIN3 isoforms in regulating distinct biological processes, however, is not well characterized. In addition, how the components of the SIN3 complex modulate the gene regulatory activity of the complex is not well understood. In this study, I identified the biological processes regulated by the SIN3 isoforms. Additionally, I explored how Caf1-55 impacts the gene regulatory activity of …

Horizontal And Vertical Integration Of Bio-Molecular Data, Tin Chi Nguyen

Wayne State University Dissertations

Modern biomedical research lies at the crossroads of data gathering, interpretation, and hypothesis testing. Due to noise, study bias, or too small changes in biological signals between disease and healthy, individual studies often fail to identify the true phenomenon. Data integration is the key to obtaining the power needed to pinpoint the biological mechanisms of disease states. Given this, we tried to make important contributions in both horizontal and vertical integration of high-throughput data; the former is meta-analysis of independent studies, while the latter is the integration of multi-omics data.

For horizontal meta-analysis, we developed two frameworks: DANUBE and the …

Network-Based Approaches To Identify The Impacted Genes And Active Interactions, Sahar Ansari

Wayne State University Dissertations

A very important step in system biology is the identification of the networks that are most impacted in the given phenotype.

Such networks explain where the target genes are affected by some other genes, and therefore describe the mechanisms involved in a biological process.

The identified networks are used to: 1) predict the disease or the responses of the system to a specific impact, 2) find the subset of genes that interact with each other and play an important role in the condition of interest, and 3) understand the mechanisms involved in that condition.

In this thesis, we propose an …

Neuronal Insult Either By Exposure To Lead Or By Direct Neuronal Damage Cause Genome-Wide Changes In Dna Methylation And Histone 3 Lysine 36 Trimethylation, Arko Sen

Wayne State University Dissertations

Prenatal and postnatal exposure to pervasive neuro-toxicants such as Lead (Pb) has been reported to causes extensive and diverse changes in the epigenetic profile. Among epigenetic modification, DNA methylation (5mC) is perhaps the most widely studied and has been proposed to be potential early biomarkers for Pb toxicity. Several studies have demonstrated the association between Pb-exposure and 5mC. However most of these studies are restricted to looking at a specific set of target genes or repetitive elements. Therefore, one of the main objectives of our study was to use an unbiased genome-wide approach to look at Pb-exposure associated changes in …

Systems Biology Approaches For The Analysis Of High-Throughput Biological Data, Michele Donato

Wayne State University Dissertations

The identification of biological processes involved with a certain phenotype, such as a disease or drug treatment, is the goal of the majority of life sciences experiments.

Pathway analysis methods are used to interpret high-throughput biological data to identify such processes by incorporating information on biological systems to translate data into biological knowledge.

Although widely used, current methods share a number of limitations.

First, they do not take into account the individual contribution of each gene to the phenotype in analysis.

Second, most of the methods include parameters of difficult interpretation, often arbitrarily set.

Third, the results of all methods …

Modeling The Mechanism Underlying Environmental And Genetic Determinants Of Gene Expression And Complex Traits, Gregory Alan Moyerbrailean

Wayne State University Dissertations

Advances in next-generation sequencing technologies and functional genomics strategies have allowed researchers to identify both common and rare genetic variation, to deeply profile gene expression, and even to determine regions of active gene transcription.

While these technologies and strategies have contributed greatly to our understanding of complex traits and diseases, there are many biological questions and analytical issues to be addressed.

Genome-wide association studies (GWAS) have successfully identified large numbers of genetic variants associated with complex traits and diseases. However, in many cases the mechanistic link between the phenotype and associated variant remains unclear. This may be because most variants …

Efficient Synergistic De Novo Co-Assembly Of Bacterial Genomes From Single Cells Using Colored De Bruijn Graph, Narjes Sadat Movahedi Tabrizi

Wayne State University Dissertations

Recent progress in DNA amplification techniques, particularly multiple displacement

amplification (MDA), has made it possible to sequence and assemble bacterial

genomes from a single cell. However, the quality of single cell genome assembly has

not yet reached the quality of normal multi-cell genome assembly due to the coverage

bias (including uneven depth of coverage and region blackout) and errors caused by

MDA. Computational methods try to mitigates the amplification bias. In this document

we introduce a de novo co-assembly method using colored de Bruijn graph,

which can overcome the problem of blackout regions due to amplification bias. The

algorithm is …

Evolution Of New Duplicate Genes In Arabidopsis Thaliana, Nicholas Curtis Marowsky

Wayne State University Theses

Abstract

Gene duplication is one of the major mechanisms by which organisms expand their genomes. The material added to the genome can then be acted upon by mutation and natural selection to increase the fitness of the species. By studying these duplicate sequences we can understand the process by which species evolve new functional genes. In a previous paper we identified 100 new duplicate genes through a genome wide comparison between A. thaliana and related species. We selected three of these new duplicate genes and investigated more closely their sequence and expression divergence from their parental gene. The three new …

Hiv Integrase Mechanisms Of Resistance To Raltegravir, Elvitegravir, And Dolutegravir, Kyla Nicole Ross

Wayne State University Theses

ABSTRACT

HIV INTEGRASE MECHANISMS OF RESISTANCE TO RALTEGRAVIR, ELVITEGRAVIR, AND DOLUTEGRAVIR

by

KYLA ROSS

December 2015

Advisor: Dr. Ladislau Kovari

Major: Biochemistry and Molecular Biology

Degree: Master of Science

HIV-1 integrase (HIV-1 IN or IN) is a multimeric enzyme that integrates the HIV-1 genome into the chromosomes of infected CD4+ T-cells. Currently there are three FDA approved HIV-1 IN strand transfer inhibitors (INSTIs) used in clinical practice: raltegravir (RAL), elvitegravir (ELV), and dolutegravir (DTG). The [Q148H], [Q148H, G140S], [Q148R], [Q148R, G140A] and [N155H, E92Q] mutations decrease IN susceptibility to RAL and ELV and may result in therapeutic failure. As an …

Plsi: A Computational Software Pipeline For Pathway Level Disease Subtype Identification, Michele Donato

Wayne State University Theses

It is accepted that many complex diseases, like cancer, consist in collections of distinct genetic diseases. Clinical advances in treatments are attributed to molecular treatments aimed at specific genes resulting in greater ecacy and fewer debilitating side effects. This proves that it is important to identify and appropriately treat each individual disease subtype. Our current understanding of subtypes is limited: despite targeted treatment advances, targeted therapies often fail for some patients. The main limitation of current methods for subtype identification is that they focus on gene expression, and they are subject to its intrinsic noise. Signaling pathways describe biological processes …

Applications Of Machine Learning In Biology And Medicine, Saied Haidarian Shahri

Wayne State University Dissertations

Machine learning as a field is defined to be the set of computational algorithms that improve their performance by assimilating data.

As such, the field as a whole has found applications in many diverse disciplines from robotics and communication in engineering to economics and finance, and also biology and medicine.

It should not come as a surprise that many popular methods in use today have completely different origins.

Despite this heterogeneity, different methods can be divided into standard tasks, such as supervised, unsupervised, semi-supervised and reinforcement learning.

Although machine learning as a field can be formalized as methods trying to …

Algorithms And Tools For Computational Analysis Of Human Transcriptome Using Rna-Seq, Nan Deng

Wayne State University Dissertations

Alternative splicing plays a key role in regulating gene expression, and more than 90% of human genes are alternatively spliced through different types of alternative splicing. Dysregulated alternative splicing events have been linked to a number of human diseases. Recently, high-throughput RNA-Seq technologies have provided unprecedented opportunities to better characterize and understand transcriptomes, in particular useful for the detection of splicing variants between healthy and diseased human transcriptomes.

We have developed two novel algorithms and tools and a computational workflow to interrogate human transcriptomes between healthy and diseased conditions. The first is a read count-based Expectation-Maximization (EM) algorithm and tool, …

The Rna Newton Polytope And Learnability Of Energy Parameters, Elmirasadat Forouzmand

Wayne State University Theses

Computational RNA secondary structure prediction has been a topic of much research interest for several decades now. Despite all the progress made in the field, even the state-of-the-art algorithms do not provide satisfying results, and the accuracy of output is limited for all the existent tools. Very complex energy models, different parameter estimation methods, and recent machine learning approaches had not been the answer for this problem. We believe that the first step to achieve results with high quality is to use the energy model with the potential for predicting accurate output. Hence, it is necessary to have a systematic …

De Novo Co-Assembly Of Bacterial Genomes From Multiple Single Cells, Narjes Sadat Movahedi Tabrizi

Wayne State University Theses

Recent progress in DNA amplication techniques, particularly multiple displacement amplication (MDA), has made it possible to sequence and assemble bacterial genomes from a single cell. However, the quality of single cell genome assembly has not yet reached the quality of normal multicell genome assembly due to the coverage bias and errors caused by MDA. Using a template of more than one cell for MDA or combining separate MDA products has been shown to improve the result of genome assembly from few single cells, but providing identical single cells, as a necessary step for these approaches, is a challenge. As a …

Teak: A Novel Computational And Gui Software Pipeline For Reconstructing Biological Networks, Detecting Activated Biological Subnetworks, And Querying Biological Networks., Thair Judeh

Wayne State University Dissertations

As high-throughput gene expression data becomes cheaper and cheaper, researchers are faced with a deluge of data from which biological insights need to be extracted and mined since the rate of data accumulation far exceeds the rate of data analysis. There is a need for computational frameworks to bridge the gap and assist researchers in their tasks. The Topology Enrichment Analysis frameworK (TEAK) is an open source GUI and software pipeline that seeks to be one of many tools that fills in this gap and consists of three major modules. The first module, the Gene Set Cultural Algorithm, de novo …

The Drosophila Interactions Database: Integrating The Interactome And Transcriptome, Thilakam Murali

Wayne State University Dissertations

In this thesis I describe the integration of heterogeneous interaction data for Drosophila into DroID, the Drosophilainteractions database, making it a one-stop public resource for interaction data. I have also made it possible to filter the interaction data using gene expression data to generate context-relevant networks making DroID a one-of-a kind resource for biologists. In the two years since the upgraded DroID has been available, several studies have used the heterogeneous interaction data in DroID to advance our understanding of Drosophila biology thus validating the need for such a resource for biologists. In addition to this, I have identified …

Towards Personalized Medicine Using Systems Biology And Machine Learning, Calin Voichita

Wayne State University Dissertations

The rate of acquiring biological data has greatly surpassed our ability to interpret it. At the same time, we have started to understand that evolution of many diseases such as cancer, are the results of the interplay between the disease itself and the immune system of the host. It is now well accepted that cancer is not a single disease, but a “complex collection of distinct genetic diseases united by common hallmarks”. Understanding the differences between such disease subtypes is key not only in providing adequate treatments for known subtypes but also identifying new ones. These unforeseen disease subtypes are …

Computational Approaches To Anti-Toxin Therapies And Biomarker Identification, Rebecca Jane Swett

Wayne State University Dissertations

This work describes the fundamental study of two bacterial toxins with computational methods, the rational design of a potent inhibitor using molecular dynamics, as well as the development of two bioinformatic methods for mining genomic data.

Clostridium difficile is an opportunistic bacillus which produces two large glucosylating toxins. These toxins, TcdA and TcdB cause severe intestinal damage. As Clostridium difficile harbors considerable antibiotic resistance, one treatment strategy is to prevent the tissue damage that the toxins cause. The catalytic glucosyltransferase domain of TcdA and TcdB was studied using molecular dynamics in the presence of both a protein-protein binding partner and …

Gene Duplication And The Evolution Of The Higher Diptera, Riyue Bao

Wayne State University Dissertations

Gene duplication is an important source of evolutionary innovation. Using the publicly available genomic information, I studied lineage-specific gene duplications in Drosophila melanogaster (fruit fly), Anopheles gambiae (mosquito), Tribolium castaneum (red flour beetle), and Apis mellifera (honey bee) at three scales: eye-specific genes, developmental genes, and genome-wide. All three studies consistently show that the Drosophila genome contains an exceptionally high number of lineage-specific yet ancient gene duplicates, the majority of which must have originated during the early diversification of the higher Diptera (Brachycera) at least 100 million years ago. Genetic data suggest that gene duplication played an important role in …

Hierarchical Multi-Label Classification For Protein Function Prediction Going Beyond Traditional Approaches, Noor Al Aydie

Wayne State University Dissertations

Hierarchical multi-label classification is a variant of traditional classification in which the

instances can belong to several labels, that are in turn organized in a hierarchy. Functional classification of genes is a challenging problem in functional genomics due to several reasons. First, each gene participates in multiple biological activities. Hence, prediction models should support multi-label classification. Second, the genes are organized and classified according to a hierarchical classification scheme that represents the relationships between the functions of the genes. These relationships should be maintained by the prediction models. In addition, various bimolecular data sources, such as gene expression data and …

Mechanistic Studies Of A Novel Ppar-Gamma Mutant That Causes Lipodystrophy And Diabetes, Olga Astapova

Wayne State University Dissertations

PPAR-gamma is a nuclear receptor that plays a central role in metabolic regulation by regulating extensive gene expression networks in adipose, liver, skeletal muscle and many other tissues. Human PPAR-gamma mutations are rare and cause a monogenetic form of severe type II diabetes with metabolic syndrome, known as familiar partial lypodystrophy. The E157D PPAR-gamma mutant causes atypical lipodystrophy in a large Canadian kindred, presenting with multiple musculoskeletal, neurological and hematological abnormalities in addition to the classic lipodystrophy features of insulin-resistant diabetes, hypertension and dyslipidemia. This mutation is localized to the p-box of PPAR-gamma, a small region that interacts directly with …

Phylogenetic Utility Of Mitochondrial And Nuclear Genes: A Case Study In The Diptera (True Flies), Jason Caravas

Wayne State University Dissertations

The value of mitochondrial versus nuclear gene sequence data in phylogenetic analysis has received much attention without yielding definitive conclusions. Theoretical arguments and empirical data suggest a lower phylogenetic utility than equivalent nuclear gene sequences, but there are also many examples of important progress made using mitochondrial sequences. We therefore undertook a systematic performance analysis of mitochondrial and nuclear sequence partitions taken from a representative sample of dipteran species. For phylogenetic tree reconstruction, mitochondrial genes performed generally inferior to nuclear genes. However, the mitochondrial genes resolved branches for which nuclear genes failed. Moreover, the combined use of mitochondrial and nuclear …