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 …

Identification And Characterization Of Novel Immune Modulating Genes In Parkinson's Disease, Kathleen Chaundy Paul Murphy

Dartmouth College Ph.D Dissertations

Parkinson’s disease (PD) is a progressive age-related neurodegenerative disorder characterized by both motor and non-motor symptoms. The poorly understood prodromal period, decades-long progression, and disease-phenotype heterogeneity continue to impede the development of preventive and curative therapies. A growing appreciation of immune system changes during the progression of PD suggests that evaluating peripheral immune cells may help identify signatures relevant to disease etiology. We aimed to expand our understanding of the peripheral immune compartment in PD via two lines of investigation: (1) by employing single-cell RNA sequencing to profile the transcriptomes of peripheral blood mononuclear cells (PBMCs) from a cohort of …

Regulation Of Müllerian Duct Mesenchyme Transcription During Mammalian Sex Differentiation, Haowen Li, Richard R Behringer, Rachel D Mullen

Dissertations and Theses (Open Access)

Sp7/Osterix (Osx) encodes a zinc-finger transcription factor of the Specificity-protein family discovered by Nakashima et al. at the MD Anderson Cancer Center. While primarily recognized for its role in osteogenesis, Osx has also been implicated in mammalian reproductive development, particularly in male sex differentiation, where Müllerian Duct (MD) regression occurs, mediated by anti-Müllerian hormone (AMH) signaling. AMH-induced regression signals are transduced by the mesenchymal tissue surrounding the ductal structure, known as the Müllerian Duct mesenchyme (MDM). It was discovered that AMH signaling is necessary and sufficient for driving Osx expression in MDM. A previous transgenic mouse reporter …

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 …

Popgenhelpr: An R Package To Streamline And Facilitate Informed Population Genomic Analyses And Visualization Of Genetic Ancestry, Diversity And Differentiation, Keaka Farleigh, Mason O. Murphy, Christopher Marie Blair, Tereza Jezkova

Publications and Research

Analysing large population genomic datasets requires an interdisciplinary skillset. Beyond a knowledge base in genetics and population biology, population genomic analyses involve computer science and statistics, representing a barrier for researchers without experience in those fields. PopGenHelpR seeks to lower this barrier by enabling researchers to perform population genomic analyses and generate near-publication quality figures in a streamlined and informed fashion. PopGenHelpR allows users to estimate genetic diversity within populations as well as differentiation among populations and individuals from single nucleotide polymorphism data. PopGenHelpR includes commonly used measures such as observed heterozygosity and FST. PopGenHelpR also provides five …

A Disorder-Aware Computational Framework To Identify Structurally Tractable Targets In Proliferative Vitreoretinopathy, Mak B. Djulbegovic, Nedym Hadzijahic, David J. Taylor Gonzalez, Michael Antonietti, Sidra Zafar, Ajay E. Kuriyan

Wills Eye Hospital Papers

OBJECTIVE: Proliferative vitreoretinopathy (PVR) remains a major cause of failure after rhegmatogenous retinal detachment repair and lacks effective pharmacologic therapies. Although epithelial-mesenchymal transition (EMT) is central to PVR pathogenesis, the structural determinants governing the tractability of EMT regulators, particularly those involving intrinsic disorder, remain poorly defined. We developed a disorder-aware, artificial intelligence-enabled computational framework to evaluate EMT-associated proteins in PVR and prioritize structurally tractable regulators for structure-based targeting.

DESIGN: A computational, hypothesis-generating study employing an in silico screening and structural modeling pipeline.

SUBJECTS: No human subjects or biological specimens were included. The dataset comprised 25 EMT-associated proteins implicated in PVR, …

Quantifying Genetic Diversity In Nematode Feeding Rate, Jacob King

Honors Theses

Understanding how populations persist under changing environmental conditions is a central question in ecology and evolutionary biology. Variation in traits related to resource acquisition may play a key role in determining population survival when resources are limited or fluctuate over time. In this study, I investigated how variation in feeding-related traits influences population persistence using a combination of computational modeling and laboratory experiments. I developed a stochastic, individual-based consumer–resource model to simulate population dynamics across varying levels of trait variance and resource regimes. Across 60,000 simulated populations, increased trait variation consistently reduced extinction risk and extended persistence time, although the …

Modeling Repeated Evolution Of Polygenic Traits, Marshall N. Hoskins

All Theses

Understanding the genetic composition of adaptive evolution is a longstanding and primary goal of evolutionary genetics. Despite many advances in our knowledge of trait adaptation, it remains unclear how demographic changes and repeated selection might alter the expected genetic architecture of adaptation. We used forward-in-time genetic simulations to test and observe the effects of opposing, repeated selection regimes on a polygenic trait in populations with and without changes in demography to better understand the genetic alleles that underlie polygenic adaptation. To further illuminate how natural populations might evolve, we investigated the impact of mutation effect size, the role of dominant/recessive …

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 …

Discovering The Genetics Underlying Speciation Traits In Heuchera (Coral Bells), Tajinder Singh

Honors Theses

Hybridization is a key evolutionary mechanism for generating biological diversity, ultimately shaping diversification and adaptation of plant lineages in natural and agroecosystems.  Our understanding of the frequency, distribution, and significance of hybridization is rapidly increasing, but the external driving forces that dictate why certain species hybridize, and others do not remain obscure. The physical traits that shape gene flow and trait sharing among species are likely to be among the key factors that directly control hybridization. Gene flow in most plants is mediated by pollinating insects and thus by pollination syndromes, which are suites of floral traits that have evolved …

A Computational Assessment Of Halobacterium Salinarum Glutamate Dehydrogenase Enzymes, Maaz Abdalla

Thesis/ Dissertation Defenses

Glutamate dehydrogenase (GDH) is a hexameric enzyme. GDH is involved in several pathways and cellular processes such as oxidation-reduction homeostasis, ammonia metabolism, lipid biosynthesis, insulin and lactate production, and acid-base equilibrium. The main objective of this thesis is to understand the structural and biochemical properties of this enzyme and why some organisms, for e.g., Halobacterium salinarum, have more than one GDH with different coenzyme specificities. The catabolism of glutamate is linked to NAD+-specific GDHs, meanwhile, NADP+-specific GDHs play an anabolic role in ammonia assimilation. Molecular docking and binding free energy calculations were employed followed by long-scale (500 nanoseconds) comparative molecular …

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.

Auditory Stimulation Rescues Cognitive Deficit In Fmr1-Ko Mice, Mohamed Ouardouz, Amanda E. Hernan, J. Matthew Mahoney, Rodney C. Scott

Department of Medicine Faculty Papers

Background/Objectives: Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by a triplet repeat expansion in the Fmr1 gene leading to the loss of Fragile X Messenger Ribonucleoprotein (Fmr1 protein). The loss of Fmr1 protein modulates many cell biological processes and leads to the emergence of intellectual disability and autism. FXS is modeled in Fmr1-KO mice that display features consistent with human FXS, including hypersensitivity, cognitive and learning deficits, hyperactivity and audiogenic seizures. Here, we investigated the effect of auditory stimulation during a range of developmental stages on recognition memory and sociability deficits in Fmr1-KO mice. Methods: Fmr1-KO mice were …

From Fair To Cure: Guidelines For Computational Models Of Biological Systems, Herbert M. Sauro, Eran Agmon, Michael L. Blinov, John H. Gennari, Joseph L. Hellerstein, Adel Heydarabadipour, Bartholomew E. Jardine, Elebeoba May, David P. Nickerson, Lucian P. Smith, Gary D. Bader, Frank T. Bergmann, Patrick M. Boyle, Andreas Dräger, James R. Faeder, Song Feng, Juliana Freire, Fabian Fröhlich, James A. Glazier, Thomas E. Gorochowski, Tomas Helikar, Henning Hermjakob, Stefan Hoops, Peter Hunter, Princess I. Imoukhuede, Sarah M. Keating, Matthias König, Reinhard Laubenbacher, Leslie M. Loew, Carlos F. Lopez, William W. Lytton, Rahuman S. Malik-Sheriff, Andrew Mcculloch, Pedro Mendes, Lealem Mulugeta, Chris J. Myers, Jerry G. Myers, Anna Niarakis, David D. Van Niekerk, Brett G. Olivier, Alexander A. Patrie, Ellen M. Quardokus, Nicole Radde, Johann M. Rohwer, Sven Sahle, James C. Schaff, Falk Schreiber, T. J. Sego, Janis Shin, Jacky L. Snoep, Rajanikanth Vadigepalli, H. Steven Wiley, Dagmar Waltemath, Ion I. Moraru

Computational Medicine Center Faculty Papers

Guidelines for managing scientific data have been established under the FAIR principles, requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model …

Computational Tools For Tandem Repeat Detection Using Long-Read Sequencing, Qian Liu, Jincheng Li

Life Sciences Faculty Research

Tandem repeats (TRs) play essential roles in a variety of biological functions, and their abnormal expansions are significantly implicated in phenotypic variation and cause >60 human diseases. However, long TR regions cannot be reliably detected using short-read sequencing, and long-read sequencing enables accurate genome-wide detection of TRs. In recent years, various computational tools have been developed to detect and genotype TRs from long-read data. In this survey, we systematically categorize and review 39 computational tools designed for TR detection, visualization and functional interpretation. We discuss their strengths and limitations for TR detection from long-read sequencing data, highlighting current challenges and …

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 …

Toward Interpretable Multi-Omics Multimodal Biomedical Artificial Intelligence, Yanjun Lyu

Computer Science and Engineering Dissertations

The complexity of human disease arises from biological processes that unfold across multiple scales, from molecular variation through cellular function, tissue organisation, brain phenotypes, each of which is associated with distinct measurement modalities, regularities, and characteristic. Contemporary biomedical artificial intelligence has brought the opportunity to reveal the complexity with in; however, its methodological default, in which models are trained on most readily available modality, does not adequately engage with the multi-scale connected structure by which biological meaning is constituted. The research area of multi-omics and multi-modal AI for biomedicine remains at an early exploratory stage, and the work presented in …

A Framework For Characterizing The Peripheral Immune Isonome Using Long-Read Single-Cell Rna Sequencing And Its Relevance To Neurological Disease, Patricia Hayes Doyle

Theses and Dissertations--Neuroscience

Long-read single-cell RNA sequencing provides an opportunity to understand human health and disease at isoform resolution, revealing cellular diversity and disease mechanisms difficult to resolve with bulk or short-read methodologies.

Using a modified PIPseq workflow and computational pipeline adapted for Oxford Nanopore (ONT) sequencing, we profiled isoform usage across immune cells, integrating marker expression and isoform discovery, generating the largest long-read single-cell dataset of human immune cells from a single individual to date. We identified non-canonical protein-coding variants of GZMB and CD3G enriched in unexpected cell types. We also discovered novel transcripts from CMC1 and LYAR with cell-type-specific signatures that …

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 …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

Sex In Plants: Birth, Death, And Trying To Survive In Changing Climates, Elena M. Meyer

Theses and Dissertations

Plant mating systems are key determinants of fitness. Angiosperms exhibit a wide variety of reproductive strategies, including self-fertilization (or selfing), outcrossing, and mixed mating. It has been estimated that up to 43% of plant species are capable of selfing, and selfing has been hypothesized to provide reproductive assurance where opportunities for mating may be limited, such as in marginal environments. However, questions regarding the overall distribution of selfing rates across angiosperms, the maintenance of mixed mating in the face of inbreeding depression, and the impact of self-fertilization on diversification rates remain incompletely resolved. Here, we explore the dynamics of self-fertilization …

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 …

Dosage Sensitivity And The Evolution Of Dosage Compensation: Tests Of The Insensitive Sex Chromosome Hypothesis In Flour Beetles, Shana Pau

Biology Dissertations

Sex chromosome evolution generates imbalances in gene dosage that can disrupt gene expression and organismal function. These imbalances are often resolved through dosage compensation mechanisms, yet the factors that drive the emergence and diversity of these systems remain poorly understood. This dissertation addresses a central question in evolutionary genomics: what governs the evolution of dosage compensation?

Focusing on dosage sensitivity as a potential driver, I evaluate the Insensitive Sex Chromosome Hypothesis (ISCH), which predicts that chromosome-wide compensation is more likely to evolve in genomic contexts that are not depleted of dosage-sensitive genes. Using flour beetles (Tribolium spp.) as a …

Interacting With Ideas: How To Engage Stem Students In Active Learning Of Theory Using Technology, Jessica Elizabeth Whitney, Keith Brian Morris

2026 Scholarly Teaching Conference: Concurrent Session Papers

STEM education in the modern age has been subject to much reform – from the integration of technology to an emphasis on student-centered teaching strategies, such as active learning. However, in the wake of virtual and blended-learning environments, student engagement and teacher assessment of student success have been challenged. Tools such as KAHOOT! and iClicker have been promoted to foster an active learning environment while sometimes falling short in regards to student retention of course material. In light of this technological educational revolution, instructors need to be able to determine the most effective tools for their discipline to aid in …