Doctor of Philosophy, The Ohio State University, 2021, Biomedical Sciences

Metabolism is a process that touches all aspects of life, from homeostasis to disease, such that the study of metabolites yields valuable insights into the inner workings of biological systems. Translating the findings of metabolomic and lipidomic experiments into biological insight, biomarkers, or actionable targets associated with disease requires functional interpretation of the data, which is challenging. One common strategy for interpreting metabolomic data is pathway enrichment analysis. Pathway analysis is useful because pathway-level perturbation can be more reproducible across samples than individual metabolite shifts, which are hindered by inconsistent experimental coverage of metabolites and functional redundancy of metabolites. However, pathway analysis of metabolites faces many barriers for success. Issues with metabolite pathway analysis include lack of metabolite pathway annotations, highly overlapping pathway definitions, and (again) lack of reproducibility in metabolite detection between experiments. Here, I present two complementary software resources, RaMP and MetaboSPAN, which I helped to develop in order to address these issues. RaMP is a metabolite annotations database that consolidates pathway, reaction, chemical structure, and other information from multiple publicly available data sources. RaMP's associated R package allows users to query information on metabolites of interest as well as perform pathway enrichment analysis using the Fisher's exact test. MetaboSPAN is an advanced pathway enrichment analysis strategy that infers activity in undetected portions of the metabolome using the vast extent of knowledge in RaMP to expand pathway-level findings and improve reproducibility between experiments. I demonstrate the utility of these tools on a metabolite data set generated in patient-derived cell lines of dedifferentiated liposarcoma with varying amplification of the MDM2 oncogene.

Committee: Ewy Mathe PhD (Advisor); Kevin Coombes PhD (Advisor); Lang Li PhD (Committee Member); Rachel Kopec PhD (Committee Member) Subjects: Bioinformatics; Biomedical Research

Doctor of Philosophy, The Ohio State University, 2022, Chemistry

Study of intermediates are vitally important for discerning reaction pathways, given their existence in transition states before the final products are formed. On the other hand, high-throughput analysis has emerged as paramount experimental necessities for research and industrial applications. The ability to study transient intermediates in a high throughput manner using ultra-small sample volumes, outside of the mass spectrometer but under controlled experimental conditions, has even bigger potential to expand the current state-of-the-art analytical measurement. This dissertation achieves this major objective in analytical method development by using selected chemical system that have importance in organic reactions that involve short-lived and isobaric intermediates, direct biofluid analysis for enhanced sensitivity, and the capture and characterization of non-volatile and labile organo-metallic intermediates of importance in Li-ion battery. There are many strategies to partially overcome these challenges described above. However, mass spectrometry has gained particular attention due to its high sensitivity, inherent selectivity, and fast response time. The introduction of electrospray and desorption electrospray ionization techniques have allowed mass spectrometry to be used for the capture of transient reaction intermediates and high-throughput analysis. Novel spray-based techniques have been developed in this dissertation that facilitate the study of reaction mechanism, both under solvent and solvent-free experimental conditions, while also offering high-throughput capabilities and the use of ultra-small sample volumes. Overall, this dissertation presents creative design and development of three methodologies as discussed in six major chapters. Chapter 2 introduces droplet imbibition platform, wherein surface effect of microdroplet is exploited by physical deposition of analyte on the surface of microdroplet with no significant diffusion to the bulk of mi (open full item for complete abstract)

Committee: Abraham Badu-Tawiah (Advisor); Amanda Hummon (Committee Member); Zachary Schultz (Committee Member); David Nagib (Committee Member) Subjects: Analytical Chemistry; Chemistry

MS, University of Cincinnati, 2020, Medicine: Biostatistics (Environmental Health)

EApp (Enrichment Application) Shiny application was developed to facilitate gene enrichment and topological pathway analysis of omics data by scientists without coding skills to support basic biological questions about biological processes and at the same time give people without coding skills the ability to perform those analyses. The application accommodates various external sources of data such as eset with expression and genes signature data, iLINCS project data, GRAIN project data, and raw data (separate data frames with rpkms, count data, and phenotypic data necessary for creating gene signatures). Shiny application provides the user with a graphical user interface that selects appropriate methods based on the source of data and calculated benchmarks. The case study included in thesis were based on TCGA data, and the benchmarks performed in the thesis provide guidelines for using set-based and network-based enrichment methods implemented in the web server.

Committee: Mario Medvedovic Ph.D. (Committee Chair); Jaroslaw Meller Ph.D. (Committee Member) Subjects: Biostatistics

Bachelor of Science (BS), Ohio University, 2024, Translational Health

Along with global rise of obesity rates among adults and adolescents, there has been an increase in weight control practices, many of which are not sustainable in the long term; thus they lead to a pattern of weight fluctuation cycles known as weight cycling. Despite the prevalence of weight cycling, little is known about its effects on health and overall mortality since survey studies in humans are conflicting. Controlled mouse studies have found increased lifespan in weight cycled (WC) mice compared to obese mice (HF). However, the cellular mechanisms responsible for increased lifespan in WC mice relative obese mice remain unknown. This thesis investigates the cellular mechanisms that underlie differences in WC and HF mice to determine why it's better to weight cycle than remain obese. Livers previously collected from a subset of low fat (LF) and high fat (HF) control mice as well as weight cycled mice sacrificed on a HF (WC(H)) diet and weight cycled mice sacrificed on a low-fat diet (WC(L)) were used. These mechanisms were evaluated by isolating RNA from the livers and then measuring gene expression levels using real-time polymerase chain reaction (RT-qPCR). RNA sequencing data was used to find activated pathways in QIAGEN Ingenuity Pathway Analysis (IPA). Results obtained from this project expanded the results to show cancer and inflammation as cellular mechanisms underlying differences in WC and HF mice. This research can be used to inform the public and healthcare professionals whether weight loss, even if not sustainable, is more healthy than stable obesity. Future applications of this study's data can inform revisions of current obesity treatment protocols and therapies.

Committee: Edward List (Advisor); Cheryl Howe (Advisor) Subjects: Biology; Biomedical Research; Cellular Biology; Nutrition

Doctor of Philosophy, The Ohio State University, 2023, Computer Science and Engineering

In many biomedical contexts, multiple types of BDMs (e.g., metabolites, genes, proteins, chromatin states, and DNA methylation sites) associate with one another directly or indirectly in groups or chains to impact phenotype or outcome. Certain significant associations often help in data interpretation and novel hypotheses generation, motivating researchers to identify the most impactful groups of BDM associations between multiple types of data. However, many state-of-the-art models focus either on individual BDM associations independently of one another or implement black box predictors of outcome that are agnostic of BDM associations. Moreover, collection of multiple types of BDMs in a subject (i.e., multi-omics data) is not always feasible, motivating the need to infer one omic type of data from another. This dissertation tackles the related problems of (1) using inter-omics approaches to infer BDM types from other related BDM types in specific contexts, (2) finding groups of multi-omics data BDMs associated with outcome through multivariate statistical analysis and graph-based predictive models, and (3) interpreting groups of multi-omics data BDMs associated with outcome in a functional context using existing knowledge. This dissertation addresses the problem of using inter-omics approaches to infer BDM types from other related BDM types in two domains of note: (1) regulatory element annotation, and (2) protein abundance prediction. First, this dissertation introduces the Self Organizing Map with Variable Neighborhoods (SOM-VN), designed to annotate regulatory elements across whole human genomes using shapes found in chromatin accessibility assays. The novelty of SOM-VN is that, while most computational tools for annotating regulatory elements require a suite of resource-intensive experimental assays, SOM-VN uses only a single assay to annotate regulatory elements. SOM-VN is validated on chromatin accessibility assays from multiple H1, HeLa, A549, and GM12878 ce (open full item for complete abstract)

Committee: Raghu Machiraju (Advisor); Ewy Mathé (Advisor); Andrew Perrault (Committee Member); Rachel Kopec (Committee Member); Rachel Kelly (Committee Member) Subjects: Applied Mathematics; Artificial Intelligence; Bioinformatics; Biomedical Research; Biostatistics; Computer Science

Doctor of Philosophy (Ph.D.), University of Dayton, 2022, Educational Leadership

American higher education institutions (HEIs) often prioritize internationalization on their campuses and as a result, prior to the outbreak of the COVID-19 pandemic, the number of international students had grown steadily over the past several decades. Pathway programs have become features in the landscape of international higher education as a mean to increase access to American higher education. Often these pathway programs are implemented and managed by third-party pathway providers, who recruit international students on a partner institution's behalf. However, little research has been conducted to explore the experiences of international students in third-party pathway programs, and this dissertation attempts to fill a gap in the literature. This study applies a three-part framework that integrates acculturation theory, sociocultural theory, and the theory that internationalization is a culture shift and utilizes Interpretative Phenomenological Analysis (IPA) through an anti-deficit lens to explore how international undergraduate students perceive and make sense of their experiences within third-party pathway programs on college campuses in the southeastern United States. Eighteen students from three research sites were interviewed, and findings indicate that (a) students rely on recruitment agencies, (b) varying degrees of alignment exist between expectations and experiences affect students' transitions, (c) pathway length varies but English development is constant, (d) sheltered spaces strengthen a sense of belonging and connectedness, (e) students are aware of and prepared for challenges, and (f) support comes in many forms.

Committee: Mary Ziskin (Committee Chair); Nicola Work (Committee Member); Matt Witenstein (Committee Member); Colleen Gallagher (Committee Member) Subjects: Educational Leadership; English As A Second Language; Higher Education

Master of Science (MS), University of Toledo, 2022, Biomedical Sciences (Bioinformatics and Proteomics/Genomics)

As multi-omics studies gain popularity, more insight is gained on the biological mechanisms that cause and influence disease. This study combined a transcriptomic approach and functional proteomic approach to gain a holistic understanding of End-Stage Renal Disease (ESRD), renal allograft status, and immunosuppression status on a molecular level. Functional proteomics approaches like kinomics consider post-translational modifications such as phosphorylation that alter protein function. The kinome is the comprehensive list of all protein kinases within an organism and the study of these protein kinases is critical in the understanding of cellular communication. Kinase activity quantified by phosphorylation can reveal key biological pathways. This study aims to assess the active kinome profiles from peripheral blood mononuclear cells (PBMCs) from renal transplant patients. Active kinome profiles of PBMCs will provide information about the cellular communication that facilitates immune response. The information gathered from the PBMC active kinome profile may also be used to predict response to immunosuppressant drugs based on the observed activity of protein kinase signaling networks. The effects of immunosuppressive drugs are well understood on a transcriptomic level but have yet to be explored on a kinomic level. Immunosuppressive drugs perturb kinase signaling networks, therefore the baseline active kinome profile might act as a biomarker for trait-based immune system function. This can then allow for the prediction of a patient's potential allograft acceptance or rejection based on their active kinome profile and how it relates to immunosuppressant drug signatures. Additionally, PBMC samples can be treated with kinase inhibitors that model the effect of an immunosuppressant medication on the protein kinase signaling networks in vitro. The impact an inhibitor has on the protein kinase activity can be used to determine optimal treatment. Furthermore, active kinome p (open full item for complete abstract)

Committee: Robert Smith (Advisor); Stanislaw Stepkowski (Committee Member); Puneet Sindhwani (Committee Member); Kunal Yadav (Committee Member) Subjects: Bioinformatics; Biomedical Research; Immunology; Medicine; Molecular Biology; Statistics

PhD, University of Cincinnati, 2021, Medicine: Biostatistics (Environmental Health)

The LINCS L1000 dataset is a large-scale compendium that contains records of the cell line specific transcriptional effects of cellular perturbation that was established to provide mechanistic and circuit-level insights with regard to cancer biology. This undertaking is a scaled-up version of the Connectivity Map (CMap) project whose goal was to connect transcriptional signatures of the downstream effects of genetic and small-molecule perturbations in a high-throughput yet cost-effective manner. This was accomplished by profiling a reduced representation of the human transcriptome – nearly 1,000 landmark transcripts whose expression is predictive of roughly 80% of non-measured genes. Whereas the choice to measure a subset of the transcriptome was primarily cost-based, reducing the representation of transcriptional data is a common method for amplifying the signal amidst the noisy background of large datasets. It can also be a valuable tool for making data amenable to a variety of bioinformatics-based analyses, for example, when lists of genes and their direction of regulation is considered based on continuously valued measurements subjected to a significance-based threshold. In the work presented in this document, we subject the records contained in the L1000 dataset to a thresholding procedure and explore how connections between over 2,000 common genetic perturbations differ between a core set of seven cancer cell lines. Specifically, we frame the connections in the context of edges between nodes in a novel adaptation of pathway-level analysis. We begin by conducting a simulation study in order to interrogate the data-generating mechanism best suited to reproduce our data of interest with the least amount of bias. This will be followed by a power analysis to assess the appropriate threshold for edge-based measurements for our dataset. Then, we will demonstrate how these measurements can be incorporated into the topology of cellular signaling pathways and (open full item for complete abstract)

Committee: Mario Medvedovic Ph.D. (Committee Chair); Marepalli Rao Ph.D. (Committee Member); John Reichard PharmD Ph.D. (Committee Member); Heidi Sucharew Ph.D. (Committee Member) Subjects: Biostatistics

Doctor of Education (Ed.D.) in Leadership Studies, Xavier University, 2020, Leadership Studies and Human Resource Development

The College Credit Plus (CCP) program, adopted and implemented by the state of Ohio in the 2015-2016 school year, presents some unique opportunities for examining the impact of accelerated college credit on preparing students for success in college. This study attempts to examine three of these unique features to examine the relationship that the CCP program might have on its participants' success in college, measured by first year GPA upon matriculation at a university. Correlational analysis was first applied to examine any relationships that might exist among the dependent and independent variables. The number of credit hours a student completed through CCP demonstrated a positive significant relationship with first year GPA, while the type of course work (General Education and Career Pathway) demonstrated a positive significant relationship but no significance was found between the two types of courses according to a Fisher's Z-Test. The researcher wanted to examine the number of years spent taking CCP courses to see if the amount of exposure of time to these types of courses demonstrated any relationship, but the data could not be determined to examine that relationship.

Committee: Gail F. Latta Ph.D. (Committee Chair); Dave Tobergte Ed.D. (Committee Member); Shirley Curtis Ed.D. (Committee Member) Subjects: Education; Education Policy; Higher Education; Secondary Education; Vocational Education

Doctor of Philosophy, Case Western Reserve University, 2020, Genetics

Vision loss is a highly feared medical condition because of its life-altering effects. Age-related vision loss is a mounting public health concern due to the growth of the elderly population. Age-related macular degeneration (AMD) is the leading cause of visual impairment in adults over 60. Family and twin studies provided significant evidence for the influence of genetic factors on AMD risk. The largest genome-wide association study (GWAS) for AMD identified 52 genomic variations associated with advanced AMD (ADV), but these variants only account for about two-thirds of AMD heritability. Therefore, we hypothesize that additional genomic loci contribute to AMD. Furthermore, GWAS alone do not directly implicate biological consequences for the associated variants. In this work, we leveraged data from the Amish population and the International AMD Genomics Consortium (IAMDGC) to interrogate the genetic architecture of AMD. Studying the Amish population enabled us to characterize a rare AMD risk variant in complement factor H (CFH P503A) and to uncover novel genomic loci for AMD in the Amish. We also built upon the known AMD loci by performing pathway analyses of the IAMDGC GWAS data. Using multiple pathway databases in our analyses, we identified biological pathways in which nominally associated AMD variants aggregated. We also computationally characterized genes that were consistently contributing to our significant pathway signals, including two novel AMD loci (PPARA and PLCG2). Variants from these statistical driver genes do not strongly contribute to ADV heritability. However, our epistasis analyses identified modest interactions between the 52 IAMDGC variants and variants in PPARA and PLCG2, which led us to hypothesize that pathway analyses of GWAS data may be useful for identifying genetic variants that contribute to AMD in a non-additive manner. This work demonstrates the utility of analyzing the genetics of a complex trait in an isolated population like the A (open full item for complete abstract)

Committee: Jonathan Haines Ph.D. (Advisor); Dana Crawford Ph.D. (Committee Chair); Thomas LaFramboise Ph.D. (Committee Member); Mark Cameron Ph.D. (Committee Member); Alexander Miron Ph.D. (Committee Member) Subjects: Bioinformatics; Biomedical Research; Genetics

Doctor of Philosophy, Case Western Reserve University, 2017, Epidemiology and Biostatistics

Interpersonal communication is a vital component of everyday life which can be negatively affected by speech sound disorders (SSD). SSD affect articulation and phonological processes, are the most common type of communication disorder, and occur in 16% of three year olds. Despite the frequency with which they occur, SSD are relatively understudied compared to other communication disorders such as dyslexia and specific language impairment. SSD can occur due to craniofacial abnormalities, hearing loss, as a symptom of certain syndromes, or due to unknown causes. SSD of unknown cause are heritable with monozygotic twin concordance rates of 0.95, but the genetic basis is not well defined. Many previous studies have focused upon FOXP2, a gene harboring a causal mutation in one large family, or genes and loci associated with language impairment (LI) or dyslexia (RD), frequently comorbid conditions. The weakness of these approaches is they are self-limiting and cannot identify novel loci. Consequently, it would be beneficial to address the etiology of SSD agnostically to identify novel loci and characterize the genetic architecture of what is likely a multifactorial disorder. To do so, data from the Cleveland Family Speech and Reading Study, a longitudinal study of children with SSD, were used to perform the first known genome-wide association study on traits associated with SSD endophenotypes in a sample ascertained based on speech sound disorder diagnosis. This analysis identified novel loci, replicated previous findings, and informed hypotheses regarding biological pathways that may be involve in SSD. To investigate the impact of LI and RD on genetic association with SSD endophenotypes, the changes in genetic effect estimates after adjusting for the conditions were analyzed. Some effects were unchanged by LI and RD status suggesting a foundational role of these loci in human communication. Finally a pathway analysis revealed similarities between SSD and oth (open full item for complete abstract)

Committee: Sudha Iyengar PhD (Committee Chair); Will Bush PhD (Committee Member); Barbara Lewis PhD (Committee Member); Catherine Stein PhD (Committee Member) Subjects: Biostatistics; Epidemiology; Genetics; Speech Therapy

Doctor of Philosophy, The Ohio State University, 2016, Chemistry

Even with extensive genomic and transcriptomic characterization of tumors, the relationship of the human cancer genotype to cancer phenotype remains unclear. Proteins, however, are the immediate molecular drivers of the cancer phenotype that govern tumorigenesis or tumor recurrence. The research described here highlights work on non-small cell lung cancer tumors and cell lines. The major goals are to discover proteins exclusive to tumor recurrence and liver kinase B1 (LKB1) gene mutation, respectively. The proteins were discovered by nanoflow multidimensional liquid chromatography coupled to mass spectrometry. The goal of the research described in Chapter 2 of the dissertation focuses on establishing a mass spectrometry-based bottom-up proteomic method for protein detection in formalin-fixed paraffin embedded (FFPE) tissue specimens. Identification of protein markers for lung cancer requires tumor tissues that are usually unavailable in the fresh, frozen state. FFPE tissues, however, are produced from resected tumor material and are readily available for proteome analysis. The use of these tumor samples in mass spectrometry demands effective sample preparation and detection strategies. In the analysis, the use of an on-slide deparaffinization method and modified lysis buffer recovered the maximum amount of protein from the slide tissue specimen and reduced the sample incubation time during digestion. Fractionation of peptide digests into fifteen high pH reversed phase fractions followed by low pH reversed phase separation resulted in the highest number of protein identifications for a minimum amount of tissue protein extract when coupled to an optimized mass spectrometry method. In Chapter 3, the use of this method for the tumor protein analysis yielded over five thousand proteins per cohort. The corresponding changes at protein level were identified by comparing the proteins discovered in specimens from recurrent to those of nonrecurrent patients. Adenocarcinom (open full item for complete abstract)

Committee: Vicki Wysocki (Advisor); David Carbone (Committee Member); Susan Olesik (Committee Member); Abraham Badu-Tawiah (Committee Member) Subjects: Chemistry

Bachelor of Science (BS), Ohio University, 2016, Biological Sciences

Type 1 Diabetes (T1D) affects 1.25 million Americans, and that number is expected to increase to 5 million by 2050. Failure to properly control blood glucose levels in T1D can result in life-threatening side effects such as kidney damage, also known as diabetic nephropathy (DN), and end-stage renal disease (ESRD). As the incident rate of T1D continues to rise worldwide, understanding DN becomes more important. This can be accomplished by examining the molecular mechanisms of damage in DN. It has been shown that the loss of STAT5 in diabetic mice exacerbates diabetic kidney damage. In this study, we used pathway analysis software to analyze gene expression results previously obtained from a microarray experiment using this diabetic STAT5 knockout (DB SKO) mouse model. We found that expression of many immune system pathways was significantly altered in the kidneys of DB SKO mice, as compared to nondiabetic and wildtype control mice. A number of different immune cell functions were also predicted to be altered. The RelA gene encoding the p65 subunit of NF¿B was predicted to be a common or “master” regulator of many of the differentially expressed genes within our dataset. Using chromatin immunoprecipitation, we found altered numbers of p65-DNA binding interactions in the promoters of differentially expressed genes within the DB SKO kidney, again in comparison to the nondiabetic and wildtype control kidneys. Therefore, our analyses indicate that STAT5 may act through RelA to affect immune system signaling pathways, resulting in an increase in inflammation and tissue damage in the absence of STAT5.

Committee: Karen Coschigano Ph.D. (Advisor) Subjects: Bioinformatics; Biomedical Research

PHD, Kent State University, 2009, College of Arts and Sciences / School of Biomedical Sciences

In mammalian cells the sequence of events and the players involved in the biosynthesis of adenosylcobalamin (AdoCbl) and methylcobalamin (MeCbl) from vitamin B12 (cyanocobalamin, CNCbl) are only partially understood. A central objective of this work was to gain a mechanistic understanding of how mammalian cells process incoming cobalamins for coenzyme biosynthesis. In Specific Aim 1 cobalamins with potential biological activity were synthesized and characterized. Nitrosylcobalamin, NOCbl, the elusive complex formed between nitric oxide and cobalamin, was synthesized via a novel reaction between aquacobalamin and the nitric oxide donor DEA-NONOate. NOCbl was obtained in high yield (85%) and purity (≥ 95% by 1H NMR spectroscopy) under alkaline and strictly anaerobic conditions. In addition to NOCbl, the synthesis and characterization of a number of other cobalamin forms were also carried out, some of which were utilized to assess the mechanisms of intracellular cobalamin processing in mammalian cells. In Specific Aim 2, a method for the accurate assessment of intracellular cobalamins, hereafter referred to as “cold-trapping”, was developed. The procedure, which was tested in cultured cells, facilitated the identification and quantification of intracellular cobalamin forms that present exchangeable beta-axial ligands. A series of in vivo and in vitro experiments describing a new role for the MMACHC gene product (cblC protein) is also presented. Our in vivo studies strongly suggested that the cblC protein is responsible for early processing of both CNCbl (decyanation) and alkylcobalamins (dealkylation). Our in vitro studies confirmed that the cblC protein catalyzed the dealkylation of Co-C bonded cobalamins by a reaction involving the nucleophilic attack of the Co-C bond by the thiolate anion of glutathione. In Specific Aim 3, I investigated the protein changes that accompany functional cobalamin deficiency in humans. The proteome of normal and cblC mutant fibroblast (open full item for complete abstract)

Committee: Donald Jacobsen PhD (Committee Chair); Nicola Brasch PhD (Advisor); Soumitra Basu PhD (Committee Member); Thomas McIntyre PhD (Committee Member); Dennis Stuehr PhD (Committee Member) Subjects: Biochemistry; Biology; Cellular Biology; Chemistry; Pathology

Doctor of Philosophy, Case Western Reserve University, 2008, Statistics

Statistical data mining is one of the most active research areas. In this thesis we develop two new data mining procedures and explore their applications to genetic data. The first procedure is called PfCluster - Profile Cluster Analysis. It is a clustering method designed for profiled genetic data. The PfCluster is efficient and flexible in uncovering clusters determined by a new class of biologically meaningful distance metrics. A new internal quality measure of clusters, coherence index, is developed to find coherent clusters. An efficient mechanism for choosing the threshold of coherent clusters is also derived and implemented. The threshold is based on the first and second order approximations to the true threshold under a null distribution for parallel clusters. The PfCluster has been applied to simulated data and two real data examples: a biomarker LOH dataset and a microarray gene expression dataset. PfCluster is competitive to the correlation-based clustering procedures. The second procedure is called RPselection - Resampling based partitioning selection. It is a feature selection algorithm designed for microarray studies. It selects a subset of genes that maximizes a fitness score. The fitness score measures the relevance between the partition labels from a clustering result and an external class label derived from the clinical outcomes. The score is computed using a resampling procedure. The RPselection algorithm has been applied to simulated data and a real uveal melanoma gene expression data. RPselection outperforms gene-by-gene test-based feature selection procedures. Software development is an integral part of modern statistical research. Two software packages, pfclust and rpselect, are developed in this thesis based on our PfCluster method and RPselection algorithm. Packages pfclust and rpselect are implemented based on R object-oriented programming framework, and they can be easily customized and extended by users. The ideas in our two procedures ca (open full item for complete abstract)

Committee: Jiayang Sun (Advisor) Subjects: Statistics