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 …

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 …

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, …

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 …

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, …

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 …

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 …