Lung cancer is the leading cause of cancer death in the US, largely due to the lack of treatment options to intercept the progression of early lung cancers and methods to diagnose lung cancer at early stages. Prior studies indicated that the lack of immune surveillance is associated with the progression of bronchial premalignant lesions (PMLs) and the gene alterations in the nasal epithelium can be leveraged for the early detection of lung cancer. Yet, the regulatory mechanism of these gene expression alterations is still less understood. Thus, there are unmet needs to study the gene expression regulation for better disease management of early lung cancer, including further understanding the biology of early lung cancer development, identifying potential interception strategies, and improving the lung cancer diagnosis. My dissertation addresses these challenges by investigating the transcriptional and post-transcriptional gene expression regulators, including transcription factors and microRNAs (miRNAs), to facilitate the understanding, interception, and diagnosis of early lung cancer. First, I explored the miRNA regulatory landscape to identify miRNA-gene regulatory relationships associated with bronchial PML progression and molecular subtypes. Using matched gene and microRNA expression profiles from patients with bronchial premalignant lesions, I identified epithelial miR-149-5p to be a key regulator of gene expression contributing to PML progression. By suppressing NLRC5, miR-149-5p inhibits MHC-I gene expression of epithelial cells, promoting early immune depletion and lesion progression. I also developed a novel statistical framework, Differential Regulation Analysis of miRNA (DReAmiR), that characterizes miRNA-mediated gene regulatory network rewiring across multiple groups from transcriptomic profiles, and identified regulatory network differences across PML molecular subtypes. Secondly, I investigated the alterations in the Hippo pathway to identify potential drug targets to intercept the progression of bronchial PMLs. I found that Hippo pathway effectors YAP/TAZ, together with transcription factors TEAD and TP63, cooperatively promote basal cell proliferation and repress signals associated with interferon responses and immune cell communication. Further in silico drug screening with external datasets identified small compounds that can reverse the direct regulated gene signature to potentially intercept bronchial PML progression. Lastly, I integrated miRNA and gene expression profiles in the nasal epithelium to distinguish malignant from benign indeterminate pulmonary nodules. I built an ensemble classifier consisting of nasal epithelial miRNA expression features, miRNA-gene top scoring pairs, and clinical features. The performance of the ensemble classifier exceeded that of the classifier built with clinical features alone. Collectively, my thesis investigated the gene expression regulation mechanisms to facilitate the understanding, interception, and diagnosis of early lung cancer pathogenesis. / 2025-11-06T00:00:00Z

Biomedical data science is a multi-disciplinary field concerned with the collection, storage, and interpretation of biomedical data that uses annotation, algorithms, and analysis to extract knowledge and insights from structured and unstructured data to be used in the development and evaluation of diagnostic tests, prognostic predictions, and therapeutic interventions. Biomedical data scientists perform this work using biomedical data that arises when samples are subjected to biochemical assays to quantitively or qualitatively investigate their pathophysiological characteristics. Increasingly, biomedical data are generated at single-cell resolution and have consequently become far more hierarchical and multimodal in nature – that is, levels of organization encapsulate one another (e.g., samples belonging to subjects are made up of cells) and multiple biological modalities are profiled simultaneously. The paradigm shift adds significant complexity to the collection, storage, management, and analysis of biomedical data, but brings with it the promise of unprecedented insights to be gained from integrative analyses. These analyses are the focus of this dissertation, where the challenges of integrating biomedical data across multiple modalities, timepoints, and studies are examined through three research projects. Challenges related to multimodal analysis of biomedical data will be explored through the development of MultimodalExperiment, a data structure that appropriately and efficiently represents multiomics data that is hierarchical, multimodal, and/or longitudinal in nature. A schematic of and methods for the data structure will be presented along with example usage to demonstrate how current challenges of alternative data structures are overcome, ease of data management is improved, and computational/storage efficiency is optimized. Challenges related to longitudinal analysis of biomedical data will be explored in the context of a cohort study of cancer patients being treated with anti-programmed cell death protein 1/programmed cell death ligand 1 immunotherapies at Boston Medical Center. The progression-free survival status of study participants will be analyzed using linear mixed effects models which incorporate longitudinal high-dimensional metabolomics data. Maps of metabolic pathways and a hypothesis will be presented to explain serum metabolites that are associated with progress-free survival status and possibly therapeutic efficacy. Challenges related to mega-analysis of biomedical data will be explored through the creation of a pipeline to preprocess transcriptomics data from human host infected with tuberculosis to support machine learning and other tasks. The details of original software developed to provide more than 10,000 samples of clean high-quality machine learning ready data from all related and eligible studies in the Gene Expression Omnibus repository will be illustrated. The importance improving diagnostic testing and therapeutic interventions for tuberculosis disease will be highlighted in the context of these data, and the specifics of why they represent a key ingredient for machine learning that helps overcome current challenges in the field will be explained.

Use of total RNA-seq in host and microbiome analysis allows for multi-omic interrogation of microbial profiles, assessment of their function and their interaction with host immune and metabolic pathways. This type of analysis calls for novel computational techniques. However, existing tools for analyzing microbial multi-omic data are lacking, as they typically address a single data type. For example, there are many available tools for the characterization of microbial communities, but these are unable to investigate microbial-host interactions. To address this need, I developed a novel computational pipeline that integrates existing methods for microbial and host expression profiling. This pipeline provides insight into possible personalized medical interventions in translational medicine. This dissertation utilized — transcriptomics and metatranscriptomics to interrogate: 1) host-microbial interactions in people with indeterminate pulmonary nodules, 2) the role of Human Endogenous Retroviruses in the early onset of ageing observed in virologically suppressed HIV positive individuals, and finally 3) to characterize humoral responses to SARS-CoV-2 peptides in Covid-19 patients. Specifically, to address the host-microbial interactions in people with indeterminate pulmonary nodules, I addressed sources of batch effects in the data, and I utilized statistical approaches to identify differentially abundant microbes in current and former smokers and malignant and benign samples. Lastly, I linked abundant microbes in both datasets to human pathways and tested for their strength of association. This approach aided in providing insight into the possible functional profile of these microbes and their role in lung cancer. Furthermore, I investigated the role of Human Endogenous Retroviruses in the early onset of ageing observed in virologically suppressed HIV positive individuals. In this project, I utilized Telescope software to generate HERVs counts. Differential analyses were then performed to identify differentially expressed HERVs in PLHIV. Using the computational pipeline that was developed for muti-omic analyses, the association of differentially expressed HERVs with pathways involved in inflammageing and inflammatory markers was then investigated. Taken together, this work identified HERVS that could act as therapeutic and diagnostic in the HIV setting. Lastly, for the third project, I sought to characterize IgG and IgM humoral responses to SARS-CoV-2 at the epitope level, where discriminating epitopes for disease severity were identified. I also investigated epitopes that were conserved between SARS-CoV-2 virus and other Human coronaviruses, allowing the investigation of associations with less severe disease outcomes. These epitopes could serve as discriminative markers for COVID-19 disease severity. / 2026-01-11T00:00:00Z

LncRNAs comprise a heterogeneous class of thousands of RNA-encoding genes whose functions are largely unknown. This thesis describes systematic computational approaches to discover liver-expressed lncRNAs globally and then deduce their regulatory roles in response to foreign chemical and hormonal exposures. In a first study, bulk liver RNA-seq data was used to discover liver-expressed lncRNAs responsive to multiple xenobiotics in a rat model. Ortholog analysis combined with co-expression data and causal inference methods was used to infer lncRNA function and deduce gene regulatory networks, including causal effects of lncRNAs on biological pathways. This work provides a framework for understanding the widespread transcriptome-altering actions of foreign chemicals in a key-responsive mammalian tissue. In a second study, single-cell RNA-seq was employed to develop a reference catalog of 48,261 mouse liver-expressed lncRNAs, a majority novel, by transcriptome reconstruction from > 2,000 bulk public mouse liver RNA-seq datasets. Single cell RNA-seq was sufficiently sensitive to detect >30,000 mouse liver lncRNAs and characterize their dysregulation in mouse models of high fat diet-induced non-alcoholic steatohepatitis (NASH), carbon tetrachloride-induced liver fibrosis, and hepatotoxicity induced by the Ah receptor agonist TCDD. Trajectory inference algorithms uncovered lncRNA zonation patterns in five major hepatic cell populations and their dysregulation in diseased states. LncRNAs expressed in NASH-associated macrophages, closely linked to disease progression, and in collagen-producing myofibroblasts, a key source of the fibrous scar in fibrotic liver, were identified. Regulatory network analysis linked individual lncRNAs with key biological pathways and gene centrality metrics identified network-essential regulatory lncRNAs in each liver disease model. In a third study, single nucleus RNA-seq combined with single nucleus ATAC-seq mapping of open chromatin regions elucidated functional linkages between cis- and trans-regulatory elements and their downstream genes targets, notably genes showing expression sex-differences impacting metabolism and disease risk. Liver cell type-specific chromatin accessibility signatures were identified, as were sex-specific accessibility signatures for hepatocytes and their associated DNA regulatory region motifs. Integrative modalities were employed to elucidate transcription factor-based mechanisms involved in sex-specific growth hormone-regulated gene expression by identifying transcriptional and epigenetic changes during feminization of mouse liver. Together, these studies characterize lncRNA function and can motivate future experiments. / 2026-03-20T00:00:00Z

<p>The success of microbial life on Earth can be attributed not only to environmental factors, but also to the surprising hardiness, adaptability and flexibility of the microbes themselves. They are able to quickly adapt to new niches or circumstances through gene evolution and also by sheer strength of numbers, where statistics favor otherwise rare events.</p><p>An integral part of adaptation is the plasticity of the genome; losing and acquiring genes depending on whether they are needed or not. Genomes can also be the birthplace of new gene functions, by duplicating and modifying existing genes. Genes can also be acquired from outside, transcending species boundaries. In this work, the focus is set primarily on duplication, deletion and import (lateral transfer) of genes – three factors contributing to the versatility and success of microbial life throughout the biosphere. </p><p>We have developed a compositional method of identifying genes that have been imported into a genome, and the rate of import/deletion turnover has been appreciated in a number of organisms. Furthermore, we propose a model of genome evolution by duplication, where through the principle of gene amplification, novel gene functions are discovered within genes with weak- or secondary protein functions. Subsequently, the novel function is maintained by selection and eventually optimized. Finally, we discuss a possible synergic link between lateral transfer and duplicative processes in gene innovation.</p>

<p>Functional genomics refers to the task of determining gene and protein function for whole genomes, and requires computational analysis of large amounts of biological data including DNA and protein sequences, protein structures and gene expressions. Machine learning methods provide a powerful tool to this end by first inducing general models from such data and already characterized genes or proteins and then by providing hypotheses on the functions of the remaining, uncharacterized cases.</p><p>This study contains four parts giving novel contributions to functional genomics through the analysis of different biological data and different aspects of biological functions. Gene Ontology played an important part in this research providing a controlled vocabulary for describing the cellular roles of genes and proteins in terms of specific molecular functions and broad biological processes.</p><p>The first part used gene expression time profiles to learn models capable of predicting the participation of genes in biological processes. The model consists of IF-THEN rules associating biological processes with minimal set of discrete changes in expression level over limited periods of time. The models were used to hypothesize new biological processes for both characterized and uncharacterized genes.</p><p>The second part investigated the combinatorial nature of gene regulation by inducing IF-THEN rules associating minimal combinations of sequence motifs common to genes with similar expression profiles. Such combinations were shown to be significantly correlated to function, and provided hypotheses on the mechanisms behind the regulation of gene expression in several biological responses.</p><p>The third part used a novel concept of local descriptors of protein structure to investigate sequence patterns governing protein structure at a local level and to predict the topological class (fold) of protein domains from sequence. Finally, the fourth part used local descriptors to represent protein structure and induced IF-THEN rule models predicting molecular function from structure.</p>

The success of microbial life on Earth can be attributed not only to environmental factors, but also to the surprising hardiness, adaptability and flexibility of the microbes themselves. They are able to quickly adapt to new niches or circumstances through gene evolution and also by sheer strength of numbers, where statistics favor otherwise rare events. An integral part of adaptation is the plasticity of the genome; losing and acquiring genes depending on whether they are needed or not. Genomes can also be the birthplace of new gene functions, by duplicating and modifying existing genes. Genes can also be acquired from outside, transcending species boundaries. In this work, the focus is set primarily on duplication, deletion and import (lateral transfer) of genes – three factors contributing to the versatility and success of microbial life throughout the biosphere. We have developed a compositional method of identifying genes that have been imported into a genome, and the rate of import/deletion turnover has been appreciated in a number of organisms. Furthermore, we propose a model of genome evolution by duplication, where through the principle of gene amplification, novel gene functions are discovered within genes with weak- or secondary protein functions. Subsequently, the novel function is maintained by selection and eventually optimized. Finally, we discuss a possible synergic link between lateral transfer and duplicative processes in gene innovation.

Functional genomics refers to the task of determining gene and protein function for whole genomes, and requires computational analysis of large amounts of biological data including DNA and protein sequences, protein structures and gene expressions. Machine learning methods provide a powerful tool to this end by first inducing general models from such data and already characterized genes or proteins and then by providing hypotheses on the functions of the remaining, uncharacterized cases. This study contains four parts giving novel contributions to functional genomics through the analysis of different biological data and different aspects of biological functions. Gene Ontology played an important part in this research providing a controlled vocabulary for describing the cellular roles of genes and proteins in terms of specific molecular functions and broad biological processes. The first part used gene expression time profiles to learn models capable of predicting the participation of genes in biological processes. The model consists of IF-THEN rules associating biological processes with minimal set of discrete changes in expression level over limited periods of time. The models were used to hypothesize new biological processes for both characterized and uncharacterized genes. The second part investigated the combinatorial nature of gene regulation by inducing IF-THEN rules associating minimal combinations of sequence motifs common to genes with similar expression profiles. Such combinations were shown to be significantly correlated to function, and provided hypotheses on the mechanisms behind the regulation of gene expression in several biological responses. The third part used a novel concept of local descriptors of protein structure to investigate sequence patterns governing protein structure at a local level and to predict the topological class (fold) of protein domains from sequence. Finally, the fourth part used local descriptors to represent protein structure and induced IF-THEN rule models predicting molecular function from structure.

<p>Being able to predict the sequence-structure relationship in proteins will extend the scope of many bioinformatics tools relying on structure information. Here we use Hidden Markov models (HMM) to recognize and pinpoint the location in target sequences of local structural motifs (local descriptors of protein structure, LDPS) These substructures are composed of three or more segments of amino acid backbone structures that are in proximity with each other in space but not necessarily along the amino acid sequence. We were able to align descriptors to their proper locations in 41.1% of the cases when using models solely built from amino acid information. Using models that also incorporated secondary structure information, we were able to assign 57.8% of the local descriptors to their proper location. Further enhancements in performance was yielded when threading a profile through the Hidden Markov models together with the secondary structure, with this material we were able assign 58,5% of the descriptors to their proper locations. Hidden Markov models were shown to be able to locate LDPS in target sequences, the performance accuracy increases when secondary structure and the profile for the target sequence were used in the models.</p>

Thesis (Ph.D.) -- University of Texas at Arlington, 2008.