Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Deconstructing Systemic Sclerosis Heterogeneity: From Patient Subtypes To Pathogenic Fibroblasts, Rezvan Parvizi

Dartmouth College Ph.D Dissertations

This thesis advances heterogeneity as the central barrier both to interpreting therapeutic response and to identifying the molecular regulators that drive fibrosis in systemic sclerosis (SSc). In SSc, heterogeneity is manifest at every level at which the disease can be measured: clinically, in the variable extent of skin and organ involvement; molecularly, in the distinct gene-expression programs that distinguish otherwise comparable patients; and cellularly, in the diverse fibroblast populations from which the fibrotic signal ultimately arises.

Leveraging longitudinal skin transcriptomics from a large cohort treated with mycophenolate mofetil (MMF), the first study demonstrates that intrinsic molecular subtypes are dynamic rather …

Accuracy Of Parameter Estimation For A Simple Gene Regulatory Network Model Is Sensitive To Network Motif, Number Of Parameters Estimated, And Magnitude And Direction Of Regulatory Relationships, Nikki C. Chun, Kam Dahlquist

Honors Thesis

A gene regulatory network (GRN) is a set of transcription factors that regulate the expression of genes encoding other transcription factors. The dynamics of a GRN explain how gene expression changes over time. GRNmap is a MATLAB software package that uses ordinary differential equations to model dynamics of small-scale GRNs. We used the program to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in three related literature-derived GRNs based on yeast cold shock microarray data previously collected in the Dahlquist Lab. We noticed large differences in estimated weight values when 1-2% of the expression values were …

Ai×Life 2026: Advances In Computational Biology Across Scales For Life & Health Sciences, Suma Mohan S., Devika N. T., Pranavathiyani G.

Conference Proceedings

Book of Abstracts of the International Conference, AIxLife 2026: Advances in Computational Biology Across Scales for Life & Health Sciences, held on 17–18 April 2026, SASTRA Deemed to be University, Thanjavur, Tamil Nadu, INDIA 

About the Conference

Conventional computational biology laid the foundation for biological data analysis through statistical models, algorithms, and curated databases, enabling insights into genes, proteins, and pathways. Today, the integration of artificial intelligence, spanning machine learning, deep learning, and large language models (LLMs) is driving a transformative shift across biological scales, from atomic and molecular interactions to pathways, cellular systems, organisms, and populations.

AI-powered approaches …

Computational And Ai Frameworks For Identifying Key Regulatory Genes And Their Target Genes In Plants And Humans, Md Khairul Islam

Dissertations, Master's Theses and Master's Reports

This dissertation presents computational and AI-driven frameworks for identifying key regulatory genes and their downstream targets across plant and human biological systems. Three studies address distinct challenges in genomic regulation using advanced machine learning and bioinformatics approaches.

The first study introduces DyGAF (Dynamic Gene Attention Focus), a dual-attention transformer framework that identifies and ranks disease-relevant biomarker genes by simultaneously modeling independent molecular responses and interdependent regulatory network behavior. Two attention models provide complementary perspectives on gene importance and are fused through a novel combination metric. Applied to COVID-19 nasopharyngeal swab profiles, the attention-weighted representations achieved 94.23% classification accuracy, high sensitivity, …

A Persistent Homology Framework For Scrna-Seq: Assessing Clustering Robustness And Quantifying Preprocessing And Integration Effects On Topological Features., Jonah Daneshmand

Electronic Theses and Dissertations

As single-cell RNA sequencing (scRNA-seq) data expands, robust methods for integrating diverse datasets are critical. This dissertation applies Persistent Homology (PH), a technique from Topological Data Analysis (TDA), to a collection of scRNA-seq datasets spanning eight tissue types to quantify how data integration affects topological features and biological interpretability. We assessed global topological structure using Betti curves, Euler characteristics, and persistence landscapes across raw, normalized, and integrated data representations. Our analysis revealed a performance inversion: while conventional methods excelled on unintegrated data, high-granularity topological methods, particularly those sensitive to global data structure, became superior after integration. This suggests a synergy …

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Computational Frameworks To Unravel The Immune Landscape, Shan He

Dissertations and Theses (Open Access)

Recent advances in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, face challenges such as resistance and immune-related adverse events, partly due to our limited understanding of the immune signaling pathways. While high- throughput genomic data provide unprecedented resolution into these immune pathways, their full potential is limited by the lack of well-annotated, context-specific immune gene sets. To address this need, I developed a workflow to construct immune gene sets by integrating RNA- seq datasets and performing decomposition. Using this approach, I constructed 28 immune- specific gene sets from 83 bulk RNA-seq datasets and 12 Natural Killer (NK) …

Predicting Genetic Interactions Using Functional Interaction Networks, Iulia Veronica Gheorghe

Dissertations and Theses (Open Access)

Mapping genetic interactions is central to understanding cellular systems and identifying therapeutic vulnerabilities, particularly in the context of cancer. Among these interactions, synthetic lethality, where simultaneous loss of two genes is lethal but loss of either alone is tolerated, offers a powerful framework for selectively targeting tumor-specific dependencies. In model organisms like S. cerevisiae, comprehensive double-knockout screens have revealed detailed genetic interaction maps, enabling systems-level insights into pathway structure, gene function, and cellular organization. Replicating this achievement in human cells, however, is complicated by the scale and complexity of the human genome. Recent advances in genome-wide CRISPR knockout screening have …

Network Analysis Of Antimicrobial Resistance In Staphylococcus Aureus: Characterization Of Hub Genes And Their Functional Implications, Md Imran Hasan, Davida Smyth, Jeong Yang, Ashley Teufel

Masters Theses (Archived)

Antimicrobial resistance is a major cause of morbidity and mortality in patients with S. aureus infections. In this study, we analyzed genes, molecular mechanisms, and pathways driving drug resistance in S. aureus using network analysis. Using whole-genome sequencing (WGS) data and systems biology approaches, we identified 229 AMR-associated genes and constructed a protein-protein interaction network among these genes. Through network topology and functional enrichment analyses, we not only confirmed their association with resistance, but also highlighted the central roles of these genes in resistance pathways, such as efflux, target replacement, and target protection, which are directly linked to multiple drug …

Integration Of Multi-Omics Datasets Evaluating Structural And Functional Features Of The Gut Microbiome In People With Hiv (Pwh)., Aakarsha Vijayakumar Rao

Electronic Theses and Dissertations

Gut dysbiosis characterized by reduced abundance of beneficial butyrate-producing bacteria has been independently linked to HIV-1 infection and heavy alcohol drinking. Further, gut dysbiosis results in loss of gut barrier integrity, microbial translocation and host-specific systemic inflammation. Therefore, to evaluate the functional consequences of structural changes in the gut microbiome, integrated data analysis is imperative. In this dissertation, we perform integrated analyses using data from multi-omics platforms to examine the structural and functional features of the gut microbiome of PWH. Gut microbiome composition was evaluated by sequencing V4 region of 16S rDNA, concentrations of metabolites and host-specific immune markers were …

Taming Biological Complexity Through The Use Of Symmetries, Luis A. Álvarez-García

Dissertations, Theses, and Capstone Projects

The study of biological systems is, inherently, the study of very complex systems. This is essentially due to the fact that they are made up of numerous, often very complicated, interactions between an extensive number of components. Often necessitating an abundance of quantitative parameters and details for a precise description. The human brain for ex- ample, consisting of ∼ 80 billion neurons with ∼ 800 to 100 trillion connections between them, each of them depending on a large set of parameters. Even simpler examples such as bacterial organisms, such as E. coli and B. subtilis, which we focus on …

Timigp: A Computational Framework To Determine The Tumor Immune Microenvironment Associated With Prognosis And Immunotherapy Response, Chenyang Li

Dissertations and Theses (Open Access)

Accumulating evidence has suggested that the tumor immune microenvironment (TIME) drastically impacts cancer patients’ clinical outcomes, including prognosis and immunotherapy response. However, understanding TIME remains challenging due to its complexity and heterogeneity. In this dissertation, we introduce TimiGP (Tumor Immune Microenvironment Illustration based on Gene Pairs), a computational framework designed to address this challenge. Leveraging single-cell RNA-seq (scRNA-seq) and bulk gene expression data alongside clinical information, TimiGP constructs a cell-cell interaction network that elucidates the relationship between immune cell function and relevant clinical outcomes, such as prognosis and treatment response. With immunological insights, these cell-cell interactions also facilitate the development …

Kreig: Quantifying The Relationship Between Sars-Cov-2 Viral Load And Infectiousness, Aurelien Marc

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Early Onset Alzheimer’S Disease Markers In Mouse Hippocampus Unveiled By Single-Cell Transcriptomic Analysis Following Cranial Radiotherapy, Tuba Aksoy

Dissertations and Theses (Open Access)

Cranial radiation therapy plays an integral role in the treatment of brain tumors but can lead to progressive cognitive deficits in survivors by mechanisms that are poorly understood. To develop preventive or mitigative strategies, it is crucial to better understand the underlying pathogenesis of radiation-induced cognitive impairments. The study investigated single-cell transcriptomics and DNA methylation changes as potential drivers of persistent cellular dysfunction after radiation exposure, specifically concentrating on the CA1-3 regions of the hippocampus and the prefrontal cortex due to their role in cognitive functions. Thirteen-week-old mice underwent whole-brain radiation at clinically relevant doses. Following whole-brain radiation, an assessment …

Dynamic Metabolic Flux Analysis Amidst Data Variability: Sphingolipid Biosynthesis Case Study In Arabidopsis Thaliana Cell Cultures, Abraham Boluwatife Osinuga

Department of Chemical and Biomolecular Engineering: Dissertations, Theses, and Student Research

Sphingolipids are pivotal for plant development and stress responses. Growing interest has been directed towards fully comprehending the regulatory mechanisms of the sphingolipid pathway. In this study, we explore its de novo biosynthesis and homeostasis in Arabidopsis thaliana cell cultures, shedding light on fundamental metabolic mechanisms. Employing ¹⁵N isotope labeling and quantitative dynamic modeling approach, we obtained data with notable variations and developed a regularized and constraint-based Dynamic Metabolic Flux Analysis (r-DMFA) framework to predict metabolic shifts due to enzymatic changes. Our analysis revealed key enzymes such as sphingoid-base hydroxylase (SBH) and long-chain-base kinase …

Cytokine Activity Estimation And Receptor Abundance Approximation With Multimodal Scrna-Seq/Cite-Seq Data., Azka Javaid

Dartmouth College Ph.D Dissertations

Single-cell RNA-sequencing (scRNA-seq) data enables individual cell resolution quantification of messenger RNA. While revolutionary for revealing key cell type and cell phenotype-specific heterogeneity, scRNA-seq data has important statistical challenges. First, scRNA-seq data is usually more sparse and second, it is more variable than bulk transcriptomics data. Given these challenges, more intuitive and interpretive statistical and computational methods are needed to develop appropriate solutions. Here, we detail the development of three techniques to perform receptor abundance estimation and cytokine activity estimation for scRNA-seq data. While SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and STREAK (gene Set Testing-based Receptor abundance …

Leveraging Redundancy As A Link Between Spreading Dynamics On And Of Networks, Felipe Xavier Costa

Electronic Theses & Dissertations (2024 - present)

A constant quest in network science has been in the development of methods to identify the most relevant components in a dynamical system solely via the interaction structure amongst its subsystems. This information allows the development of control and intervention strategies in biochemical signaling and epidemic spreading. We highlight the relevant components in heterogeneous dynamical system by their patterns of redundancy, which can connect how dynamics affect network topology and which pathways are necessary to spreading phenomena on networks. In order to measure the redundancies in a large class of empirical systems, we develop the backbone of directed networks methodology, …

Investigating The Protein Interactome Of Escherichia Coli To Understand Metabolic Regulation When Proteins Bind: A Systems Biology Study., Shomeek Chowdhury

Theses and Dissertations

Proteins bind other proteins and work together as machines to perform many biological functions. Metabolic enzymes governing the life-sustaining metabolic reactions and therefore, metabolism are proteins which also binds other proteins. In this dissertation work, I have explored proteins when they interact with metabolic enzymes, play any role in regulating the cell’s metabolism. I study this phenomenon in Escherichia coli whose protein-protein interaction data is readily available in public databases with proper reference to the experimental study. Another good reason is the predictions we make in this computational study can be tested and validated in E. coli as it is …

Determining The Effects Of Glycocalyx Modifications On The Electrophysical Properties Of Human Mesenchymal Stem Cells, Rominna E. Valentine Ico

Electronic Theses, Projects, and Dissertations

Human mesenchymal stem cells (hMSCs) have gained popularity in clinical trials due to their multipotent differentiation characteristics, ability to secrete bioactive molecules, migrate into diseased or damaged tissues, and their immunosuppressive properties. HMSC cultures are heterogeneous, containing stem cells, partially differentiated progenitor cells, and fully differentiated cells. One of the major challenges with hMSCs therapeutic potential is the inability to select specific cell subpopulations due to an insufficient number of biomarkers. Often the biomarkers used, like those for fluorescence-activated cell sorting, are not sufficient to define hMSCs because they overlap with other cell types. Consequently, there is a need to …

Interpretable Mechanistic And Machine Learning Models For Pre-Dicting Cardiac Remodeling From Biochemical And Biomechanical Features, Anamul Haque

All Dissertations

Biochemical and biomechanical signals drive cardiac remodeling, resulting in altered heart physiology and the precursor for several cardiac diseases, the leading cause of death for most racial groups in the USA. Reversing cardiac remodeling requires medication and device-assisted treatment such as Cardiac Resynchronization Therapy (CRT), but current interventions produce highly variable responses from patient to patient. Mechanistic modeling and Machine learning (ML) approaches have the functionality to aid diagnosis and therapy selection using various input features. Moreover, 'Interpretable' machine learning methods have helped make machine learning models fairer and more suited for clinical application. The overarching objective of this doctoral …

Gene Expression–Based Algorithms For The Identification Of Drug Combinations In Personalized Medicine, Lon Fong

Dissertations and Theses (Open Access)

Three of the major problems facing cancer therapeutics are 1) drug resistance, the intrinsic or acquired ability of cancer cells to evade the effect of the therapies used to treat them; 2) heterogeneity among individual patients’ disease at the molecular level and the resulting variability in therapeutic response; and 3) the limitations of genomics biomarkers in matching patients to the most effective therapy. One possible solution to drug resistance is the use of combination therapies rather than monotherapies. Use of multiple drugs, each with a different mechanism of action, lowers the chances that the cancer cells will develop or have …

Data-Driven Biomarker Panel Discovery In Ovarian Cancer Using Heterogenous Data Fusion On Exosomal And Non-Exosomal Microrna Expression Data, Paritra Mandal

All Dissertations

Ovarian cancer (OC) is an aggressive gynecological cancer and is currently the 5th leading cause of deaths due to cancer in women. High mortality rates are attributable to the vague pathogenesis and asymptomatic nature of the early stages. The development of a liquid biopsy for routine OC screening could help identify the disease at an earlier stage, making treatments more likely to be effective thereby increasing survival rates. Exosomes, small (~100nm) extracellular vesicles present in body fluids, have been shown to contain cancer-progression, onset, and related factors, making them good candidates for use in liquid biopsies. However, to date, only …

Methods And Tools To Improve Performance Of Plant Genome Analysis, Drew Ferrell

Theses and Dissertations

Multi -omics data analysis and integration facilitates hypothesis building toward an understanding of genes and pathway responses driven by environments. Methods designed to estimate and analyze gene expression, with regard to treatments or conditions, can be leveraged to understand gene-level responses in the cell. However, genes often interact and signal within larger structures such as pathways and networks. Complex studies guided toward describing dynamic genetic pathways and networks require algorithms or methods designed for inference based on gene interactions and related topologies. Classes of algorithms and methods may be integrated into generalized workflows for comparative genomics studies, as multi -omics …

The Effects Of Host-Like Environmental Signals And Gene Expression On Capsule Growth In Cryptococcus Neoformans, Yu Min Jung

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cryptococcus neoformans is a fungal pathogen that causes cryptococcosis, a disease that kills almost 200,000 people worldwide each year. A unique feature of this deadly yeast is its polysaccharide capsule, which is known to be associated with its virulence. Here, we systematically explore the effects of all possible combinations of 4 capsule-inducing signals on gene expression, cell size, and capsule size. These signals are medium (YPD, DMEM or RPMI), temperature (30°C or 37°C), CO₂ (room air or 5%), cAMP (0 mM or 20 mM), and pH buffer (HEPES/no HEPES). We explore the effects of exogenous cAMP at a range …

Exploiting Chemogenetic And Genetic Interactions In Human Cells As An Avenue For New Therapeutic Opportunities, Medina Colic

Dissertations and Theses (Open Access)

The advent of CRISPR technology and its adaptation to the mammalian genome made whole-genome knockout screens possible directly in human cells. Gene knockout answers how essential that gene is for cell fitness and proliferation. Genes showing moderate to severe fitness defects are called essential genes and provide insights into disease-specific candidate therapeutic targets. Additionally, CRISPR offers other applications for genome editing. Two applications this dissertation is based on are 1) combination of gene knockout and drug treatment, which enables the identification of chemogenetic interactions, or gene mutations that enhance or suppress the activity of a drug, and 2) combinatorial editing, …

Bone Marrow Stroma-Induced Transcriptome Signatures Of Multiple Myeloma As Modulated By Junb, Jasleen Kaur Gandhi

Graduate Theses, Dissertations, and Problem Reports (ETD)

The bone marrow (BM) microenvironment acts as a breeding ground for drug resistance in multiple myeloma (MM). The interaction with bone marrow stromal cells (BMSCs) confer environment-mediated drug resistance (EMDR) to multiple myeloma. We investigated BM stroma-induced transcriptome signatures of MM cells through a sophisticated analysis of gene expression. In particular, we defined transcription program modulated by JunB, an emerging regulator of MM pathogenesis and a member of the transcription factor superfamily activator protein 1 (AP-1), in response to BM stimulation. The data and results lay down a foundation for future studies to illustrate the regulatory role of JunB in …

Immunological Factors Associated With Siv/ Shiv Persistence In Diverse Tissue Niches, Omalla A. Olwenyi

Theses & Dissertations

The significant challenge towards a successful HIV cure lies in eradicating persistent viral reservoirs across diverse tissue niches. As a result, HIV-infected individuals have to resort to lifelong antiretroviral therapy. Recent news of supposed HIV eradication in a second patient has further re-invigorated the fields of HIV cure. However, a few barriers remain, such as the lack of currently available assays to accurately quantify viral reservoirs, limited information on cellular factors associated with persistence, and varied dynamics of the viral reservoir in various body compartments. Lastly, HIV-infected individuals live different lifestyles stemming from comorbid substance abuse including consumption of morphine, …

Machine Learning-Based Risk Factor Analysis And Prevalence Prediction Of Intestinal Parasitic Infections, Ahmet Ay

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Investigation Of Arabidopsis Extremophyte Relatives, Schrenkiella Parvula And Eutrema Salsugineum Reveals Different Roads Leading To Salt Stress Tolerance, Kieu-Nga Thi Tran

LSU Doctoral Dissertations

How plants adapt to salt stress has been a central question in plant biology for decades. Yet we have not been able to fully understand the molecular networks and genetic mechanisms underlying this complex trait. Most of the genetic work on salinity stress has focused on understanding salt stress responses in the leading, yet a salt-sensitive model Arabidopsis thaliana. With the recent availability of genomes for wild-relatives of A. thaliana, we can now investigate how naturally salt adapted plants may have evolved modified or novel molecular networks to adapt to salt stress. Therefore, my research utilizes a comparative …