Computational Biology Commons^™

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 …

Data Curation And Integration For Cancer Cell Line Pharmacogenomics Analysis, Meric Kinali

Graduate Masters Theses

Cancer cell lines are essential resources for connecting genomic and molecular features with drug response and identifying biomarkers. Therefore, large-scale cancer cell line resources have been growing, but this introduces challenges such as data standardization, consistent annotation, matching identifiers, cross-database integration, and a reproducible computational framework. CellMiner Cross Database (CellMinerCDB) integrates pharmacogenomic datasets from multiple resources and provides a standardized analysis of cell lines across datasets. This thesis examines cancer cell line cross-databases and their importance in pharmacogenomics, with a particular focus on CellMinerCDB. As part of this work, I developed an R package for the gCSI (Genentech Cell Line …

Biologically Informed Negative Samplingfor Antibody Chain Pairing Classification, Ishita Singh

Computer Science Senior Theses

Antibody heavy and light chain (H/L) pairing is fundamental to antigen recognition and stability. While single-cell sequencing preserves native pairing information, widely used bulk repertoire and spatial transcriptomics platforms do not, motivating the need for efficient ML methods to infer H/L pairing. Training a binary classifier for this task faces the methodological challenge of a lack of true biological negatives, since natural selection eliminates B cells with incompatible H/L pairs.

In this thesis, I introduce a biologically informed negative sampling strategy for H/L pairing classification, drawing on known V-gene biases in heavy and light chain pairing. Pseudo-negatives are constructed by …

Large Scale Kmer-Based Proteomic Analysis: An Application Towards Evolutionary Constraint Discovery, Matthew Chak

Master's Theses

Large protein databases now make it possible to study short peptides across natural protein sequence space at unprecedented scale, but exhaustively counting k-mers across billions of protein sequences remains computationally difficult. This thesis develops an exact amino-acid k-mer counting method based on direct addressing, in which fixed-length amino-acid strings are encoded as base-20 integers and updated with a sliding-window recurrence. By avoiding key storage and collision resolution, this approach removes overhead inherent to hash-map-based methods when the k-mer space is sufficiently dense. A memory analysis shows when direct addressing is preferable to open-addressing hash tables, and expected-saturation calculations motivate its …

Understanding Epigenomic Landscapes In Cancer Progression And Immunotherapy Response, Jonathan Schulz

Dissertations and Theses (Open Access)

Nonmutational epigenomic reprogramming has emerged as a key hallmark of cancer that plays crucial roles in tumor evolution during its progression and response to therapy. However, the extent and nature of epigenomic reprogramming remains poorly understood. This dissertation examines how epigenetic regulation shapes cancer progression and response to immunotherapy. Working at the intersection of cancer biology and computational genomics, it develops analytical frameworks for characterizing chromatin structure and DNA methylation across diverse tumor contexts and uses these frameworks to address two complementary biological questions: how promoter-associated chromatin organization varies across cancer types, and how epigenetic perturbation modulates tumor immunogenicity in …

Detecting Cancer Genes Using Graph Neural Networks, Marvin Masabo Nkaka

Posters - 2026

• Cancer survival prediction is challenging due to the complexity of genomic data and limited samples especially for rarer cancer types. • To address this challenge, we developed an Artificial Neural Network (ANN) model for survival analysis using RNA-sequencing gene expression data from The Cancer Genome Atlas (TCGA). • Moreover, a key concept we investigate was how transfer learning enhanced our model’s performance especially for rarer cancer types difficult to perform accurate survival analysis due to their limited samples.

Characterization And Advancement Of Molluscan Venom Gland Cell Model Systems, James V. Parziale

Dissertations, Theses, and Capstone Projects

Venomous mollusks, including cone snails and coleoid cephalopods, produce rich repertoires of bioactive peptides with profound effects on cellular physiology, yet there are few tractable experimental systems that exist to study their development, maintenance, and venom secretion. Although therapeutics such as the cone snail peptide Ziconotide demonstrate the biomedical potential of molluscan venoms, progress in understanding how venom glands form, function, and evolve has been hindered by the lack of reliable husbandry and the complete absence of in vitro cell models. Modern comparative genomics has revealed widespread convergent evolution across venomous lineages, but molluscan datasets remain underrepresented, limiting insights into …

Computational Comparative Genome Analysis Reveals The Characteristics Of Acinetobacter Baumannii C123 Strains, Jennilyn Nicole G. Mendoza

The Lasallian Journal of Health

Acinetobacter baumannii is a Gram-negative, aerobic and multi-drug resistant bacterial pathogen commonly associated with nosocomial infections. This species has several strains and is known to cause pneumonia, septi cemia, meningitis, urinary tract infection and wound infection which are associated with high mortality rates. This study focused on a genome-wide compari son among target C123 strain and 15 other reference strains to predict the overall properties, and resistance mechanisms of the C123 strain. A total of 16 whole genome sequence strains were retrieved from NCBI database for analysis. The selected strains were assem bled and annotated using computational tools. Further more, …

Searching For Cause Of Unexplained Protein Interaction With Multiple Assembly Methods, Gavin Anderson

Master's Projects

Research into colistin adjuvants has identified interactions between a host of proteins and proteins found within certain strains of bacteria. BLAST, a widely adopted local sequence alignment tool, was used to create a database containing the genomes of relevant bacterial strains, which would then be used to query against the previously identified proteins. Many of these strains do not have publicly available assemblies, which makes database construction difficult. CATwalk, a new naïve fragment extender, is first described in this paper and used to extend fragments of interest. BLAST results first identified short fragments with sufficient similarity, which can then be …

Metabolic Syndrome Beyond Diagnostic Criteria: Population-Scale Integrative Metabolomics Characterization, Marwa Talal

Theses and Dissertations

Background: Metabolic syndrome (MetS) is a complex cluster of interrelated metabolic abnormalities associated with elevated cardiometabolic risk. While diagnosis is based on well-established five clinical criteria, these may overlook early or atypical metabolic alterations. Large-scale metabolomic profiling offers an opportunity to identify biochemical signatures of MetS beyond diagnostic bias and to evaluate their relative importance across different presentations of the syndrome.

Methods: Data from 117,147 UK Biobank participants were analyzed in a cross-sectional design. High-throughput NMR quantified 75 circulating metabolites, for. Univariate analyses, MetS subtype stratification, and elastic net models with SHAP interpretation were applied to assess feature …

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, …

Systematics Of Disk-Winged Bats (Thyroptera), Courtney L. Klumpp

Capstone Showcase

This thesis investigates the evolutionary relationships among disk-winged bats in the genus Thyroptera, a small group of Neotropical bats known for the adhesive suction disks on their wings and feet that allow them to roost inside tightly rolled leaves. The distinctive morphology and behavior of these bats have not been extensively documented, and their phylogenetic relationships remain only partially resolved. The goal of this research is to better understand how these species are related to one another and to determine whether the five recognized species of Thyroptera form a single monophyletic group. To address this question, morphological and genetic …

Data From: Complete Mitochondrial Genomes Of The Spring Pygmy Sunfish (Elassoma Alabamae), Kayla M. Fast, David Lee Pounders, Mayah P. Peterson, Michael W. Sandel

Research Data

The Spring Pygmy Sunfish, Elassoma alabamae (Mayden, 1993), is a small species of sunfish (Centrarchiformes) endemic to tributaries to the middle Tennessee River in north Alabama. Elassoma alabamae is the most geographically restricted member of Elassoma and the only species found above the fall line. This species was twice considered extinct and was listed under the Endangered Species Act (ESA) as threatened in 2013. To date, surveys for this species have been limited to traditional invasive methods using dipnets and seines. In order to reduce impacts on sensitive populations, we sequenced the mitochondrial genome with the intent of developing a …

Identifying Rna Splicing Changes During Alcohol Withdrawal Using An Optimized Rna-Seq Analysis Pipeline, Yasaswi Veera, Luana Martins De Carvalho, Amy Lasek

Undergraduate Research Posters

Alcohol use disorder (AUD) causes long-lasting changes in brain gene expression and RNA splicing, particularly in the ventral hippocampus. This study analyzes RNA-Seq data from rats exposed to chronic alcohol and withdrawal to identify transcript-level and splicing alterations. An optimized RNA-Seq pipeline using STAR, FeatureCounts, edgeR, and WGCNA improved efficiency by 25-40% while maintaining consistency across 25 datasets. Results reveal changes in neural signaling and stress-response pathways associated with withdrawal. This work provides both biological insight into AUD and a reproducible computational framework for transcriptomic analysis.

Recurrence And Co-Occurrence Of Enhancer-Promoter Interactions Across Human Samples, Satvik Gunjala

Honors Undergraduate Theses

Experimental mapping of enhancer-promoter interactions (EPIs) is resource-intensive, and current computational prediction methods struggle with intrinsic genomic complexity and reliance on limited training data. To address this bottleneck and provide insights for improved computational methods, this study systematically analyzed chromatin contact datasets to investigate the recurrence and co-occurrence of enhancer-promoter interactions across human samples. Putative interactions were evaluated across two HiChIP datasets comprising 218 total samples and one Hi-C dataset comprising 266 samples to assess recurrence across samples and assess sequencing depth related to unique EPIs. Additionally, a preliminary item-based collaborative filtering recommender model was developed to assess co-occurrence patterns …

Generating Predictive Gene Expression Signatures For Alzheimer's Disease Using Postmortem Brain Tissue, Ashley Duche

Pharmaceutical Sciences (PhD) Dissertations

Background: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ) plaques and tau protein aggregates. These pathological features develop in specific brain regions, but why some areas are more vulnerable to early AD-related changes remains unclear. To address this, predictive gene expression signatures were developed to explore the molecular mechanisms underlying regional susceptibility to AD pathology.

Methods: This was performed using postmortem brain (PMB) tissue from participants in the Religious Orders Study and Memory and Aging Project (ROSMAP), Mayo Clinic, and Mount Sinai Brain Bank (MSBB) to generate gene expression signatures from six brain …

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 …

Population Structure Analysis Of Four Basal Higher-Attines Using Bioinformatic Approaches, Gabriel Mcdanield

Biology Theses

Metapopulations within the family Formicidae are unique among other animals due to the large colonies they build, and the mating strategies required to fertilize a specialized reproductive caste that will often produce young for the life of the colony. One group of ants, the fungus-gardeners (tribe Attini). The population structure of North American non-leafcutting, fungus-gardening ants has been understudied, especially in the southwest of the United States. Additionally, not much is known about their dispersal biology, so the dynamics of dispersal of these species and how they affect population structure is likewise not well known. To shed light on the …

Bioinformatic Analysis Of Pogz Variants In Relation To White Sutton Syndrome, Hannah Rollins

Theses

White-Sutton syndrome (WHSUS) is a rare neurodevelopmental disorder caused by mutations in the Pogo Transposable Element with ZNF Domain (POGZ) gene, which encodes pogo-transposable element with ZNF domain, a chromatin regulator essential for proper mitotic progression and DNA repair. This study uses a bioinformatic framework to evaluate the structural and functional impact of missense mutations in the conserved amino acid region (positions 500–800) of the POGZ protein. Protein modeling, variant effect prediction, conservation analysis, and molecular dynamics simulations were employed to gain an understanding of the effects of POGZ missense mutations on protein structure and movement with specific emphasis on …

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 …

Performance Analysis Of Computational Methods For Predicting Protein Function In Rare Diseases, Aichetou Mohamed Sidiya, Hanin Alzaher, Razan Almahdi, Tayeb Brahimi

Effat Undergraduate Research Journal

Protein function prediction is crucial for understanding the underlying mechanisms of rare diseases. With the increasing availability of computational methods including machine learning-based approaches, network-based methods, and sequence-based methods, predicting protein functions has become more accessible. However, it is not clear which of these methods performs better or how they compare to each other in terms of accuracy, efficiency, and scalability. In this study, we evaluate several computational methods for predicting protein functions in rare diseases using key performance indicators (KPIs). We analyze the strengths and weaknesses of each method and provide recommendations for researchers and clinicians interested in using …

Quantile Index Predictors Using R Package Hyper.Gam, Tingting Zhan, Misung Yi, Inna Chervoneva

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

MOTIVATION: Evaluation of single-cell protein expression from immunohistochemistry images is used increasingly in biomedical research. Many proteins are used solely for phenotyping cells in the tumor microenvironment. Other proteins with meaningfully quantitative expression levels provide so-called functional protein biomarkers. There is still a limited number of methods and software tools available for utilizing the entire distributions of single-cell expression levels.

RESULTS: We present the R package hyper.gam, providing a supervised learning framework for deriving biomarkers based on single-cell distribution quantiles. The single-cell data are first converted into sample quantile functions, which are then used as predictors in scalar-on-function regression models …

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 …

An Integrative Genomics Approach For The Discovery Of Potential Clinically Actionable Diagnostic And Prognostic Biomarkers In Colorectal Cancer, Mark Fertel, Duaa Mohammad Alawad, Chindo Hicks

School of Graduate Studies Faculty Publications

Background: Despite remarkable progress in clinical management of patients and intensified screening, colorectal cancer remains the second most common cause of cancer-related death in the United States. The recent surge of next generation sequencing has enabled genomic analysis of colorectal cancer genomes. However, to date, there is little information about leveraging gene expression data and integrating it with somatic mutation information to discover potential biomarkers and therapeutic targets. Here, we integrated gene expression data with somatic mutation information to discover potential diagnostic and prognostic biomarkers and molecular drivers of colorectal cancer. Methods: We used publicly available gene expression and somatic …

Cazyme Gene Cluster Diversity In Human Gut Microbiome, Yi Xing

Department of Food Science and Technology: Dissertations, Theses, and Student Research

In gut microbiome research, carbohydrate-active enzyme gene clusters (CGCs) have emerged as key functional units for understanding microbial glycan degradation. Unlike taxonomic or broad pathway annotations, CGCs offer gene-cluster-level resolution and capture substrate-specific microbial functions. However, their diversity and distribution in relation to host metabolic phenotypes, such as obesity, remain poorly characterized. This study tests the hypothesis that the composition and abundance of fiber-targeting CGCs vary between obese and healthy human gut microbiomes, reflecting distinct microbial carbohydrate utilization strategies. To examine this, we constructed a high-quality reference CGC dataset comprising 94,019 clusters from the Unified Human Gastrointestinal Genome and profiled …

Topodino: Self-Supervised Topological Representation Learning For Neuronal Morphologies, Yasser Binbisher

Master's Theses

Neuronal cell types are categorized by transcriptomic identity, yet their morphological heterogeneity defies this classification. In response, researchers have adopted unsupervised graph representation learning as a tool to reveal morphological variation within single-class transcriptomic types. However, the complex geometry of neuronal morphology—especially long axons and dense dendrites—challenges graph neural networks, which struggle with message propagation across extended structures. To mitigate this, current approaches enforce sub-sampling on neuronal graphs and omit axons entirely, sacrificing critical biological features for computational efficiency. To overcome this trade-off, this thesis introduces TopoDINO, a self-supervised, topology-aware representation learning model designed to preserve the full hierarchical organization …

Analytical Approaches For Identification Of Essential Genomes Of Plasmodium Knowlesi And Babesia Divergens, Sida Ye

Graduate Doctoral Dissertations

Apicomplexa constitute a large phylum of single-celled, obligate intracellular protozoan parasites. Notably, Plasmodium spp. and Babesia spp. are apicomplexan parasites that infect red blood cells. Plasmodium species are the causative agent of malaria and are transmitted by Anopheles mosquitoes, affecting large human populations, whereas Babesia spp., transmitted through the bite of Ixodes ticks cause babesiosis.

In this dissertation, we investigate the essential genome of these parasites using high-throughput transposon mutagenesis. Identifying the essential genome is key to finding new drug targets and understanding resistance mechanisms, a crucial pursuit given the rising resistance to frontline antimalarial drugs and the challenges …

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 …

Investigating The Effects Of Transcription Factor Binding And Genetic Variants In The Striatum Of Post-Mortem Cohorts With Opioid Use Disorder, Rajashree Chakraborty

Theses & Dissertations

The opioid crisis has emerged as one of the most pressing public health challenges of our time, with Opioid Use Disorder (OUD) affecting millions of lives across the globe. Studying OUD is not merely an academic pursuit but a critical necessity in addressing this multifaceted epidemic. The urgency of this research is underscored by the staggering prevalence of OUD, with an estimated 3.7% of U.S. adults requiring treatment in 2022 alone. Despite the availability of effective medications for OUD, a significant treatment gap persists, with only a quarter of those in need receiving these life-saving interventions. The far-reaching consequences of …

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 …