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Tesi sul tema "Cancer bioinformatics"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 13 febbraio 2022

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1

Webber, James Trubek. "Cancer Bioinformatics for Biomarker Discovery." Thesis, University of California, San Francisco, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10604636.

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<p> Cancer is a complex and multifaceted disease, and a vast amount of time and effort has been spent on characterizing its behaviors, identifying its weaknesses, and discovering effective treatments. Two major obstacles stand in the way of progress toward effective precision treatment for the majority of patients.</p><p> First, cancer's extraordinary heterogeneity—both between and even within patients—means that most patients present with a disease slightly different from every previously recorded case. New methods are necessary to analyze the growing body of patient data so tha

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2

Wang, Leying. "Noncoding RNA-Involved Interactions for Cancer Prognosis: A Prostate Cancer Study." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586651927830285.

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3

Wu, Tsung-Jung. "Integration of Cancer-Related Mutations for Pan-Cancer Analysis." Thesis, The George Washington University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1556905.

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<p> Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as T

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4

Pepin, Francois. "Bioinformatics approaches to understanding the breast cancer microenvironment." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=92240.

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Breast cancer is a complex disease that requires the acquisition of several traits in order to proliferate and spread to nearby and distant tissues. However, many combinations are possible, making it harder to determine their significance. Genome-wide approaches such as gene expression profiling have provided an unbiased and global tool to investigate those traits, allowing investigators to both separate tumors into biologically meaningful categories and then to investigate their features in that context. A well-organized effort is required in order to collect and analyze the large number of s

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5

Liao, Peter Lee Ming Liao. "Bioinformatics approaches to cancer biomarker discovery and characterization." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1525694252170957.

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6

Zacharouli, Markella-Achilleia. "Characterization of immune infiltrate in early breast cancer based on a multiplex imaging method." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417716.

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Breast cancer is the most common type of cancer among women worldwide. Multiple studies have reported the role of tumor-immune interactions and mechanisms that the immune system uses to combat tumor cells. Therapies based on the immune response are evolving by time, but more research is required to understand and identify the patterns and relationships within the tumor microenvironment. This study aims to characterize immune cell expression patterns using a multiplex method and to investigate the way different subpopulations in breast cancer patients’ tissue samples are correlated with clinico

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7

Hillerton, Thomas. "Predicting adverse drug reactions in cancer treatment using a neural network based approach." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15659.

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8

Pestana, Valeria. "Modeling drug response in cancer cell linesusing genotype and high-throughput“omics” data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166744.

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9

Stetson, Lindsay C. "Computational Approaches for Cancer Precision Medicine." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1428050439.

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10

Bebek, Gurkan. "Functional Characteristics of Cancer Driver Genes in Colorectal Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1495012693440067.

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11

Mayrhofer, Markus. "Copy Number Analysis of Cancer." Doctoral thesis, Uppsala universitet, Institutionen för medicinska vetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-244361.

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By accurately describing cancer genomes, we may link genomic mutations to phenotypic effects and eventually treat cancer patients based on the molecular cause of their disease, rather than generalizing treatment based on cell morphology or tissue of origin. Alteration of DNA copy number is a driving mutational process in the formation and progression of cancer. Deletions and amplifications of specific chromosomal regions are important for cancer diagnosis and prognosis, and copy number analysis has become standard practice for many clinicians and researchers. In this thesis we describe the dev

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12

Andersson, Claes. "Fusing Domain Knowledge with Data : Applications in Bioinformatics." Doctoral thesis, Uppsala universitet, Centrum för bioinformatik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8477.

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Massively parallel measurement techniques can be used for generating hypotheses about the molecular underpinnings of a biological systems. This thesis investigates how domain knowledge can be fused to data from different sources in order to generate more sophisticated hypotheses and improved analyses. We find our applications in the related fields of cell cycle regulation and cancer chemotherapy. In our cell cycle studies we design a detector of periodic expression and use it to generate hypotheses about transcriptional regulation during the course of the cell cycle in synchronized yeast cultu

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13

Murat, Katarzyna. "Bioinformatics analysis of epigenetic variants associated with melanoma." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17220.

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The field of cancer genomics is currently being enhanced by the power of Epigenome-wide association studies (EWAS). Over the last couple of years comprehensive sequence data sets have been generated, allowing analysis of genome-wide activity in cohorts of different individuals to be increasingly available. Finding associations between epigenetic variation and phenotype is one of the biggest challenges in biomedical research. Laboratories lacking dedicated resources and programming experience require bioinformatics expertise which can be prohibitively costly and time-consuming. To addres

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14

Veanes, Margus. "Identification of novel loss of heterozygosity collateral lethality genes for potential applications in cancer." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-433768.

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Over the course of this project, I demonstrate the utility of a 4-phase analysis pipeline in the context of cancer therapy and the associated search for antineoplastic drug candidates. I showcase a repeatable means for generating lists of potential targets which may be used in conjunction with methods like small molecule screening as part of a search for broadly effective antineoplastic agents. By using publicly available variant call format (VCF) data sourced from the 1000 genomes project, global human population-wide data for non-sex chromosomes was filtered and transformed in a 4-phase pro

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15

Novak, Barbara Anna. "Quantitative pathway modeling and analysis in cancer." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3261242.

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16

Janvid, Vincent. "Building a genomic variant based prediction model for lung cancer toxicity." Thesis, KTH, Tillämpad fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297411.

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Since the completion of the the Human genome project in 2003, the evident complexity of our genome and its regulation has only grown. The idea that having sequenced the human genome would solve this mystery was quickly discarded. With the decreasing costs of DNA sequencing, a plethora of new methods have evolved to further understand the role of non-coding regions of our genome, which makes up 98% its length. Genetic variations in these regions are therefore abundant in the human population, but their e ects are hard to characterize. Many non-coding variants have been linked to complex disease

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17

Raplee, Isaac D. "Contribution of Retrotransposons to Breast Cancer Malignancy." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7900.

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The components contributing to cancer progression, especially the transition from early to invasive are unknown. Consequently, the biological reasons are unclear as to why some patients diagnosed with atypia and ductal carcinoma in situ (DCIS) never progress into invasive breast cancer. The “one gene at a time” approach does not sufficiently predict progression. To elucidate the early stage progression to invasive ductal cancer, expression signature of transcripts and transposable elements in micropunched samples of formalin-fixed, paraffin embedded (FFPE) tissue was conducted. A bioinformatic

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18

Chan, Simon Kit. "A bioinformatics meta-analysis of differentially expressed genes in colorectal cancer." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/379.

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BACKGROUND: Elucidation of candidate colorectal cancer biomarkers often begins by comparing the expression profiles of cancerous and normal tissue by performing high throughput gene expression profiling. While many such studies have been performed, the resulting lists of differentially expressed genes tend to be inconsistent with each other, suggesting that there are some false positives and negatives. One logical solution to this problem is to determine the intersection of the lists of differentially expressed genes from independent studies. It is expected that genes that are biologically rel

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19

Ballinger, Tracy J. "Analysis of genomic rearrangements in cancer from high throughput sequencing data." Thesis, University of California, Santa Cruz, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3729995.

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<p> In the last century cancer has become increasingly prevalent and is the second largest killer in the United States, estimated to afflict 1 in 4 people during their life. Despite our long history with cancer and our herculean efforts to thwart the disease, in many cases we still do not understand the underlying causes or have successful treatments. In my graduate work, I’ve developed two approaches to the study of cancer genomics and applied them to the whole genome sequencing data of cancer patients from The Cancer Genome Atlas (TCGA). In collaboration with Dr. Ewing, I built a pipel

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20

Kaur, Jaspreet. "IDENTIFICATION OF MUTATIONAL LANDSCAPES IN AFRICAN AMERICAN TRIPLE-NEGATIVE BREAST CANCER." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1523652587887506.

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21

Skander, Dannielle. "Integrative 'Omics Approach to Investigate Relationship Between COPD and Lung Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1559950959673037.

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22

Lesurf, Robert. "Molecular pathway analysis of mouse models for breast cancer." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32499.

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Human breast cancer is an extremely heterogeneous disease, consisting of a number of different subtypes with varying levels of aggressiveness reflected by distinct, but largely undefined, molecular profiles. Here we have analyzed several novel mouse models for breast cancer in the context of the human subtypes, and have shown parallels between the mice and humans at numerous biologically relevant levels. In addition, we have developed a statistical framework to help elucidate the individual molecular comp

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23

Johnsson, Anna. "Mining for Lung Cancer Biomarkers in Plasma Metabolomics Data." Thesis, Linköping University, Biotechnology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57670.

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<p>Lung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gain

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24

Wang, Chao. "Integrative Analysis of Multi-modality Data in Cancer." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429791373.

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25

Rahpeymai, Neda. "Data Mining with Decision Trees in the Gene Logic Database : A Breast Cancer Study." Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-710.

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<p>Data mining approaches have been increasingly used in recent years in order to find patterns and regularities in large databases. In this study, the C4.5 decision tree approach was used for mining of Gene Logic database, containing biological data. The decision tree approach was used in order to identify the most relevant genes and risk factors involved in breast cancer, in order to separate healthy patients from breast cancer patients in the data sets used. Four different tests were performed for this purpose. Cross validation was performed, for each of the four tests, in order to evaluate

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26

Reddy, Veena K. "Analysis of single cell RNA seq data to identify markers for subtyping of non-small cell lung cancer." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18514.

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Single cell RNA technology is a recent technical advancement used to understand the cancertumorgenicity at single cell resolution. In this study we have analyzed the scRNA data from thenon-small cell lung cancer (NSCLC) dataset to facilitate the early identification of NSCLCsubtypes namely, squamous cell carcinoma (SCC) and adenocarcinoma (AC). Non-immunecells, have a major role in tumorigenesis of the malignant tumors, in early stages. Therefore,we have analyzed the major non-immune cells, namely endothelial cells and fibroblast cellsfrom the GSE127465 dataset using SEURAT pipeline. Dimension

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27

Ding, Hao. "Visualization and Integrative analysis of cancer multi-omics data." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1467843712.

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28

Stamouli, Sofia. "Mathematical modeling of normal and cancer prostate signaling pathways." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-257431.

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The field of systems biology has become very popular as a means to deal with cancer as well as other complex biological issues. It enables scientists to gain an insight into difficult conditions through mathematical approaches that have been developed. Prostate cancer is the second leading cause of death among men after skin cancer and its heterogeneity makes it a complex disease. In this study we focus on three pathways known to play crucial roles in the formation of prostate cancer. By using a mathematical model that combines all of them we describe the interactions taking place during signa

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29

Guan, Xiaowei. "Bioinformatics Approaches to Heterogeneous Omic Data Integration." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1340302883.

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30

Zucker, Mark Raymond. "Inferring Clonal Heterogeneity in Chronic Lymphocytic Leukemia From High-Throughput Data." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554049121307262.

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31

Nibbe, Rod K. "Systems Biology of Human Colorectal Cancer." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1264179836.

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32

Pique, Daniel Gonzalo. "Deriving Novel Insights from Genomic Heterogeneity in Cancer." Thesis, Yeshiva University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=11014739.

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<p> Cancer is a leading cause of morbidity and mortality, and one in three individuals in the U.S. will be diagnosed with cancer in their lifetime. At the molecular level, cancer is driven by the activity of oncogenes and the loss of activity of tumor suppressors. The availability of genomic data from large sets of tumor tissue have facilitated the identification of subgroups of patients whose tumors share molecular patterns of expression. These molecular signatures, in turn, can help identify clinically-useful patient subgroups and inform potential therapeutic strategies against cancer.</p><p

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33

Bhat, Akshay [Verfasser]. "Bioinformatics modeling of proteomics changes in muscle invasive bladder cancer / Akshay Bhat." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2016. http://d-nb.info/1113011882/34.

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34

Zhang, Yi. "NOVEL APPLICATIONS OF MACHINE LEARNING IN BIOINFORMATICS." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/83.

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Technological advances in next-generation sequencing and biomedical imaging have led to a rapid increase in biomedical data dimension and acquisition rate, which is challenging the conventional data analysis strategies. Modern machine learning techniques promise to leverage large data sets for finding hidden patterns within them, and for making accurate predictions. This dissertation aims to design novel machine learning-based models to transform biomedical big data into valuable biological insights. The research presented in this dissertation focuses on three bioinformatics domains: splice ju

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35

Zichner, Thomas. "Building graph models of oncogenesis by using microRNA expression data." Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1167.

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<p>MicroRNAs (miRNAs) are a class of small non-coding RNAs that control gene expression by targeting mRNAs and triggering either translation repression or RNA degradation. Several groups pointed out that miRNAs play a major role in several diseases, including cancer. This is assumed since the expression level of several miRNAs differs between normal and cancerous cells. Further, it has been shown that miRNAs are involved in cell proliferation and cell death.</p><p>Because of this role it is suspected that miRNAs could serve as biomarkers to improve tumor classification, therapy selection, or p

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36

Swenson, Hugo. "Detection of artefacts in FFPE-sample sequence data." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392623.

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Next generation sequencing is increasingly used as a diagnostic tool in the clinical setting. This is driven by the vast increase in molecular targeted therapy, which requires detailed information on what genetic variants are present in patient samples. In the hospital setting, most cancer diagnostics are based on Formalin Fixed Paraffin Embedded (FFPE) samples. The FFPE routine is very beneficial for logistical purposes and for some histopathological analyses, but creates problems for molecular diagnostics based on DNA. These problems derive from sample immersion informalin, which results in

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37

Marwaha, Shruti. "A Genomics and Mathematical Modeling Approach for the Study of Helicobacter Pylori associated Gastritis and Gastric Cancer." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439308645.

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38

Carr, Nicole. "Data Pooling to Identify Differentially Expressed Genes in Lung Cancer of Nonsmokers." University of Toledo Health Science Campus / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=mco1461881266.

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39

Alles, Marie Chehani Clinical School St Vincent's Hospital Faculty of Medicine UNSW. "A bioinformatics approach to discovery of estrogen-responsive genetic pathways in breast cancer." Awarded by:University of New South Wales. Clinical School - St Vincent's Hospital, 2008. http://handle.unsw.edu.au/1959.4/41513.

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Breast cancers fall into two major classes depending on their estrogen receptor (ER) status. ER+ and ER- tumors have very different molecular phenotypes, and may have distinct cells of origin. ER- tumors generally fail to respond to endocrine therapy and have a poorer prognosis. To develop a comprehensive understanding of the gene networks active in ER+ compared to ER- breast cancers, we performed a meta-analysis of Grade 3 breast cancers from five published datasets. A measure of association with ER status taking into account intra- and inter-study variability was calculated for every probe s

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40

Howe, Eleanor Arden. "MicroRNA expression and activity in high-grade serous ovarian cancer." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:9d17590c-550b-4ae9-ac8d-15387cf70e5f.

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miRNAs are critical modulators in the development and progression of cancer. Emerging evidence suggests that they are drivers of ovarian cancer. A better understanding of the molecular underpinnings of the development, progression and chemoresistance of the disease is critical for the development of new, more eﬀective therapies. Here we explore the expression patterns of miRNAs as they relate to gene expression, as they diﬀer across molecular subtypes of the disease. We examine the correlation structure of miRNA expression with mRNA expression in two distinct genomic datasets and report on pat

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41

Zack, Travis Ian. "Exploring cancer's fractured genomic landscape| Searching for cancer drivers and vulnerabilities in somatic copy number alterations." Thesis, Harvard University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3645095.

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<p> Somatic copy number alterations (SCNAs) are a class of alterations that lead to deviations from diploidy in developing and established tumors. A feature that distinguishes SCNAs from other alterations is their genomic footprint. The large genomic footprint of SCNAs in a typical cancer's genome presents both a challenge and an opportunity to find targetable vulnerabilities in cancer. Because a single event affects many genes, it is often challenging to identify the tumorigenic targets of SCNAs. Conversely, events that affect multiple genes may provide specific vulnerabilities through "bysta

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42

Olsen, Catharina. "Causal inference and prior integration in bioinformatics using information theory." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209401.

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An important problem in bioinformatics is the reconstruction of gene regulatory networks from expression data. The analysis of genomic data stemming from high- throughput technologies such as microarray experiments or RNA-sequencing faces several difficulties. The first major issue is the high variable to sample ratio which is due to a number of factors: a single experiment captures all genes while the number of experiments is restricted by the experiment’s cost, time and patient cohort size. The second problem is that these data sets typically exhibit high amounts of noise.<p><p>Another impor

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43

Vaidya, Priyanka S. "Artificial Intelligence Approach to Breast Cancer Classification." University of Akron / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=akron1240957599.

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44

Tofigh, Ali. "Using Trees to Capture Reticulate Evolution : Lateral Gene Transfers and Cancer Progression." Doctoral thesis, KTH, Beräkningsbiologi, CB, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10608.

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The historic relationship of species and genes are traditionally depicted using trees. However, not all evolutionary histories are adequately captured by bifurcating processes and an increasing amount of research is devoted towards using networks or network-like structures to capture evolutionary history. Lateral gene transfer (LGT) is a previously controversial mechanism responsible for non tree-like evolutionary histories, and is today accepted as a major force of evolution, particularly in the prokaryotic domain. In this thesis, we present models of gene evolution incorporating both LGTs an

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45

Vang, Yeeleng Scott. "An Ensemble Prognostic Model for Metastatic, Castrate-Resistant Prostate Cancer." Thesis, University of California, Irvine, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10162542.

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<p> Metastatic, castrate-resistant prostate cancer (mCRPC) is one of the most prevalent cancers and is the third leading cause of cancer death among men. Several treatment options have been developed to combat mCRPC, however none have produced any tangible benefits to patients' overall survivability. As part of a crowd-sourced algorithm development competition, participants were asked to develop new prognostic models for mCRPC patients treated with docetaxel. Such results could potentially assist in clinical decision making for future mCRPC patients. </p><p> In this thesis, we present a ne

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46

Patel, Vishal N. "Colon Cancer and its Molecular Subsystems: Network Approaches to Dissecting Driver Gene Biology." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1310087563.

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47

Momin, Amin Altaf. "Application of bioinformatics in studies of sphingolipid biosynthesis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34842.

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The studies in this dissertation demonstrate that the gene expression pathway maps are useful tools to notice alteration in different branches of sphingolipid biosynthesis pathway based on microarray and other transcriptomic analysis. To facilitate the integrative analysis of gene expression and sphingolipid amounts, updated pathway maps were prepared using an open access visualization tool, Pathvisio v1.1. The datasets were formatted using Perl scripts and visualized with the aid of color coded pathway diagrams. Comparative analysis of transcriptomics and sphingolipid alterations from experim

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48

Srivastava, Arunima. "Univariate and Multivariate Representation and Modeling of Cancer Biomedical Data." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577717365850367.

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49

Sharpnack, Michael F. Sharpnack. "Integrative Genomics Methods for Personalized Treatment of Non-Small-Cell LungCancer." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523890139956055.

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Al-Khudhair, Ahmed S. "Anti PD-1/PD-L1 Immunotherapy, New Era in the Fight Against Cancer: Genomic and Transcriptomic Exploration." University of Toledo Health Science Campus / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=mco1568910675816609.

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