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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 that we can classify each new patient with as much accuracy and precision as possible. In chapter 2 I present a method that integrates data from multiple genomics platforms to identify axes of variation across breast cancer patients, and to connect these gene modules to potential therapeutic options. In this work we find modules describing variation in the tumor microenvironment and activation of different cellular processes. We also illustrate the challenges and pitfalls of translating between model systems and patients, as many gene modules are poorly conserved when moving between datasets.</p><p> A second problem is that cancer cells are constantly evolving, and many treatments inevitably lead to resistance as new mutations arise or compensatory systems are activated. To overcome this we must find rational combinations that will prevent resistant adaptation before it can start. Starting in chapter 3 I present a series of projects in which we used a high-throughput proteomics approach to characterize the activity of a large proportion of protein kinases, ending with the discovery of a promising drug combination for the treatment of breast cancer in chapter 8.</p><p>
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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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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 The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Due to the petabytes of data and sequence information present in NGS primary databases, a High-performance Integrated Virtual Environment (HIVE) platform for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31,979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13,896 small scale and 308,986 large scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies.</p>
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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 samples necessary for such analyses. The Bioinformatics Integrated Application Software represents a way to facilitate both the organization of laboratory manipulation and automating subsequent analyses.<br>A large part of the complexity of breast cancer comes from the different types of cells that constitute the microenvironment and participate in diverse ways to tumor progression. Blood vessels play an important role in tumor progression, as additional vessels are necessary to support tumor growth. However, those new vessels are generally immature and often cannot efficiently provide nutrients to the tumor. This thesis shows that there exist two classes of tumor blood vessels that are associated with vessel maturity and differ in their expression of several antiangiogenic drug targets.<br>Numerous interactions occur between the various components of the tumor microenvironment. Using matched expression profiles of these cell types, it is possible to iden- tify specific processes that involve several cell types, such as Th1 and Th2 immune responses. This first step will open the door to a better mapping of the interactions and signals that occur in breast cancer.<br>Le cancer du sein est une maladie complexe qui requiert l'accumulation de plusieurs caractéristiques avant de pouvoir se multiplier et envahir les tissues rapprochés et éloignés. Plusieurs combinaisons sont par contre possibles, compliquant la tâche de d ́eterminer leurs importances. Les techniques d'analyse sur tout le génome comme l'expression génique sont des outils globaux et non biaisés pour étudier ces caractéristiques. Elle permettent de séparer les tumeurs en groupes biologiquement significatifs et d'étudier leurs caractéristiques dans ce contexte. Un effort concerté est nécessaire pour collecter et analyser la grande quantité de tumeurs requise. Le "Bioinformatics Integrated Application Software" est un système qui permet d'organiser les manipulations de laboratoire et d'automatiser les analyses ultérieures.<br>Une large proportion de la complexité du cancer du sein provient des diff ́erentes espèces de cellules faisant partie du microenvironnement et participant à la progression de la tumeur. Les vaisseaux sanguins jouent un rôle important dans la progression du cancer car des vaisseaux additionels sont nécessaires pour supporter la croissance tumorale. Ces vaisseaux sont par contre généralement immatures et ne peuvent souvent pas alimenter efficacement la tumeur. Cette thèse démontre qu'il existe deux catégories de vaisseaux sanguins tumoraux qui sont associées avec la maturité des vaisseaux et différent dans leur expression de gènes cibles de plusieurs médicaments antiangiogenèses.<br>De nombreuses interactions se produisent entre les différentes composantes du microenvironnement tumoral. L'utilisation de profils d'expressions concordants de différentes espèces cellulaires rend possible l'identification de procédés impliquant plusieurs espèces cellulaires, incluant des réactions immunitaires de types Th1 et Th2. Cette première étape va ouvrir la porte à une meilleure connaissance des échanges de signaux dans le cancer du sein.
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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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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 clinicopathological characteristics. The results of this study indicate that there must be an association within immune cell composition and clinicopathological characteristics (Estrogen Receptor Status (ER+/ER-), Progesterone Receptor (PR+/PR-), Grade (I,II,III), which is a way to characterize the cancer cells on how similar they look to normal ones, Menopause, Tumor size, Nodal status, HR status, HER2) but validation in larger patient population is required in order to evaluate the role of the immune infiltration as a predictive / prognostic biomarker in early breast cancer.
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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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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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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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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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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 development of two computational methods, TAPS and Patchwork, for analysis of genome-wide absolute allele-specific copy number per cell in tumour samples. TAPS is used with SNP microarray data and Patchwork with whole genome sequencing data. Both are suitable for unknown average ploidy of the tumour cells, are robust to admixture of genetically normal cells, and may be used to detect genetic heterogeneity in the tumour cell population. We also present two studies where TAPS was used to find copy number alterations associated with risk of recurrence after surgery, in ovarian cancer and colon cancer. We discuss the potential of such prognostic markers and the use of allele-specific copy number analysis in research and diagnostics.
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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 cultures as well as investigate if domain knowledge about gene function can explain whether a gene is periodically expressed or not. We then generate hypotheses that suggest how periodic expression that depends on how the cells were perturbed into synchrony are regulated. The hypotheses suggest where and which transcription factors bind upstreams of genes that are regulated by the cell cycle. In our cancer chemotherapy investigations we first study how a method for identifiyng co-regulated genes associated with chemoresponse to drugs in cell lines is affected by domain knowledge about the genetic relationships between the cell lines. We then turn our attention to problems that arise in microarray based predictive medicine, were there typically are few samples available for learning the predictor and study two different means of alleviating the inherent trade-off betweeen allocation of design and test samples. First we investigate whether independent tests on the design data can be used for improving estimates of a predictors performance without inflicting a bias in the estimate. Then, motivated by recent developments in microarray based predictive medicine, we propose an algorithm that can use unlabeled data for selecting features and consequently improve predictor performance without wasting valuable labeled data.
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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 address
this, we have developed a collection of freely available Galaxy tools
(Poterlowicz, 2018a), combining analytical methods into a range of convenient
analysis pipelines with graphical user-friendly interface.The tool suite
includes methods for data preprocessing, quality assessment and differentially
methylated region and position discovery. The aim of this project was to
make EWAS analysis flexible and accessible to everyone and compatible with
routine clinical and biological use. This is exemplified by my work undertaken
by integrating DNA methylation profiles of melanoma patients (at baseline and
mitogen-activated protein kinase inhibitor MAPKi treatment) to identify novel
epigenetic switches responsible for tumour resistance to therapy (Hugo et
al., 2015). Configuration files are publicly published on our GitHub repository
(Poterlowicz, 2018b) with scripts and dependency settings also available to
download and install via Galaxy test toolshed (Poterlowicz, 2018a). Results
and experiences using this framework demonstrate the potential for Galaxy to
be a bioinformatics solution for multi-omics cancer biomarker discovery tool.
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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 process to obtain high population frequency, heterozygotic, nonsynonymous single nucleotide variants (nsSNVs) residing in functional domains of proteins. Through manual filtration combined with software-assisted annotation, I obtained a ranked list of 50 top scoring annotated variants across the human autosome, all residing in known protein domains. Additionally, a single top variant was selected for proof-of-concept structure prediction and visualization. When the methodology outlined herein is coupled to additional loss-of-heterozygosity (LOH) prevalence data across cancer genomes, it may be used to identify candidate variants which collectively represent potential loss-of-heterozygosity based collateral lethalities (CL) in the underlying cancer. Furthermore, under the assumption that subsequent methods like small molecule screening succeed in finding molecule(s) targeting a structural aspect of one of these variants, any subsequently developed therapeutic approaches may possess broader therapeutic utility dependent upon the strictness of the initial heterozygotic filtering threshold applied at the onset of the project pipeline. When combined with additional cancer data, the recreation of such gene lists at other degrees of heterozygotic thresholding can allow for the creation of lists of autosomal loss-of-heterozygosity gene candidates, representing potential collateral lethality targets with varied degrees of utility dependent upon the strictness of the initial filtration threshold.
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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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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 diseases such as cancer predisposition. This thesis aims to investigate the potential e ects of non-coding variants on drug toxicity, that is, how severe the adverse e ects of a drug are to the treated patients. More specifically it will study the effects of two cancer drugs, Gemcitabine and Carboplatin, on a set of 96 patients with lung cancer. To do this we use spatial data acquired by the promoter-targeting method HiCap as well as expression data obtained from blood cell lines. Using the variants obtained through whole genome sequencing of the patients, a supervised learning approach was attempted to predict the final toxicity experienced by the patients. The large number of variants present among the comparably few patients resulted in poor accuracy. The conclusion was drawn that the resolution of HiCap is too low compared to the density of variants in the non-coding regions. Additional data, such as transcription factor Chip-Seq data, and transcription factor motifs are needed to locate potentially contributing variants within the interactions.<br>Sedan den första sekvenseringen av det mänskliga genomet 2003 har vår bild av vårt genom och hur det regleras bara blivit mer komplex. Iden om att ha tillgång till ett helt genom skulle losa detta mysterium förkastades snabbt. Med de sjunkande kostnaderna for sekvensering har ett brett utbud av nya metoder utvecklats for att bättre förstå de icke-kodande regionernas roll i v art genom. Då dessa regioner utgör98% av vårt DNA ar innehåller de stor variation bland det mänskliga släktet, men att förutsaga deras effekt är mycket svårt. Många icke-kodande variationer har kopplats till komplexa sjukdomar så som ökad risk för cancer.Denna uppsats syftar till att undersoka de potentiella effekterna av icke-kodande varianter på hur allvarliga biverkningar en patient får av en cancerbehandling. Närmare undersöks två mediciners, Gemcitabins och Carboplatins effekt på 96 lungcancerpatienter. För detta används spatial data samt genuttrycksdata från blodcellinjer.Med utgångspunkt från genetiska varianter bland patienternas sekvenserade genom testades övervakad inlärning för att förutsäga graden av biverkningar hos patienterna. Den stora mängden varianter som bärs av de förhållandevis få patienterna resulterade i låg träffsäkerhet hos prediktorn. Slutsatsen drogs att upplösningen av HiCap är för låg i jämförelse med den höga densiteten av varianter i icke-kodanderegioner. Mer data, så som Chip-Seq data från transkriptionsfaktorer samt deras specifika bindningsekvenser behövs för att lokalisera varianter inom en interaktion, som potentiellt skulle kunna påverka biverkningarna.
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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 bioinformatics pipeline to analyze poor quality, short reads (>36 nts) from RNA-Seq data was created to compare the most common tools for alignment and differential expression. Most samples from patients prepared for RNA-seq analysis are acquired through archived FFPE tissue collections, which have low RNA quality. The pipeline analytics revealed that STAR alignment software outperformed others. Furthermore, our comparison revealed both DESeq2 and edgeR, with the estimateDisp function applied, both perform well when analyzing greater than 12 replicates. Transcriptome analysis revealed progressive diversification into known oncogenic pathways, a few novel biochemical pathways, in addition to antiviral and interferon activation. Furthermore, the transposable element (TE) signature during breast cancer progression at early stages indicated long terminal repeat (LTRs) as the most abundantly differentially expressed TEs. LTRs belong to endogenous retroviruses (ERV), a subclass of TEs. The retroviral and innate immune response activity in DCIS, which indirectly corroborates the increase in ERV expression in this pre-malignant stage. Finally, to demonstrate the potential role of TEs in the transition from pre-malignant to malignant breast cancer we used pharmacological approaches to alter global TE expression and inhibit retrotransposition activity in control and breast cancer cell lines. It was expected that dysregulation of TEs be associated with increased invasiveness and growth. However, our results indicated that DNA methyltransferase inhibitor 5-Azacytidine (AZA) consistently retarded cell migration and growth. While unexpected, these findings corroborate recent studies that AZA may induce an interferon response in cancer via increased ERV expression. This body of work illustrates the importance of understanding bioinformatics methods used in RNA-seq analysis of common clinical samples. These studies suggest the potential for TEs as biomarkers for disease progression and novel therapeutic approach to investigate in additional model systems.
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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 relevant to cancer tumorigenesis will be reported most often, while sporadically reported genes are due to the inherent biases and limitations of each of the profiling platforms used. However, the statistical significance of the observed intersection among many independent studies is usually not considered. PURPOSE: To address these issues, we developed a computational meta-analysis method that ranked differentially expressed genes based on the following criteria, which are presented in order of importance: the amount of intersection among studies, total tissue sample sizes, and average fold change in expression. We applied this meta-analysis method to 25 independent colorectal cancer profiling studies that compared cancer versus normal, adenoma versus normal, and cancer versus adenoma tissues.
RESULTS: We observed that some genes were consistently reported as differentially expressed with a statistically significant frequency (P <.0001) in the cancer versus normal and adenoma versus normal comparisons, but not in the cancer versus adenoma comparison. We performed a review of some of the high ranking candidates and determined that some have previously been shown to have diagnostic and/or prognostic utility in colorectal cancer. More interestingly, the meta-analysis method also identified genes that had yet to be tested and validated as biomarkers. Thus, these candidates are currently being validated at the protein level on colorectal tissue microarrays.
CONCLUSION: Our meta-analysis method identified genes that were consistently reported as differentially expressed. Besides identifying new biomarker candidates, our meta-analysis method also provides another filter to remove false positive genes from further consideration. In conclusion, the genes presented here will aid in the identification of highly sensitive and specific biomarkers in colorectal cancer.
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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 pipeline to detect retrotransposon insertions from paired-end high-throughput sequencing data and found somatic retrotransposon insertions in a fifth of cancer patients. </p><p> My second novel contribution to the study of cancer genomics is the development of the CN-AVG pipeline, a method for reconstructing the evolutionary history of a single tumor by predicting the order of structural mutations such as deletions, duplications, and inversions. The CN-AVG theory was developed by Drs. Haussler, Zerbino, and Paten and samples potential evolutionary histories for a tumor using Markov Chain Monte Carlo sampling. I contributed to the development of this method by testing its accuracy and limitations on simulated evolutionary histories. I found that the ability to reconstruct a history decays exponentially with increased breakpoint reuse, but that we can estimate how accurately we reconstruct a mutation event using the likelihood scores of the events. I further designed novel techniques for the application of CN-AVG to whole genome sequencing data from actual patients and applied these techniques to search for evolutionary patterns in glioblastoma multiforme using sequencing data from TCGA. My results show patterns of two-hit deletions, as we would expect, and amplifications occurring over several mutational events. I also find that the CN-AVG method frequently makes use of whole chromosome copy number changes following by localized deletions, a bias that could be mitigated through modifying the cost function for an evolutionary history. </p>
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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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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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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 components that are at play across a panel of human breast or murine mammary tumors. Our results indicate that, while no mouse model captures all aspects of the human disease, they each contain components that are shared by a subset of human breast tumors. Furthermore, our statistical framework provides numerous advantages over previous methodologies, in helping to reveal the individual molecular pathways that make up the biology of the tumors.<br>Le cancer du sein est connue pour être une maladie très hétérogène, composé d'un nombre de différents sous-types avec différents niveaux de l'agressivité et distinctes, mais indéfini, profils moléculaires. Ici, nous avons analysé plusieurs nouveaux modèles de souris pour le cancer du sein, dans le cadre des sous-types, et nous avons trouver des parallèles à un certain nombre de niveaux pertinents biologiques. En outre, nous avons développé une méthodologie statistique pour aider à élucider les différents composants moléculaires qui sont à jouer dans un groupe de tumours de sein d'humains ou mammaires murins. Nos résultats indiquent que, même si aucun modèle de souris capte tous les aspects de la maladie chez l'homme, chacun contiennent des composants qui sont partagées par un sous-ensemble de tumeurs mammaires humaines. En outre, notre outil statistique offre de nombreux avantages par rapport aux précédentes méthodes, pour aider à révéler les voies moléculaires qui composent la biologie des tumeurs.
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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 gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.NyckelordLung 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 gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.Nyckelord</p>
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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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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 the capacity of the decision tree approaches in correctly classifying ‘new’ samples. In the first test, the expression of 108 breast related genes, shown in appendix A, for 75 patients were used as input to the C4.5 algorithm. This test resulted in a decision tree containing only four genes considered to be the most relevant in order to correctly classify patients. Cross validation indicates an average accuracy of 89% in classifying ‘new’ samples. In the second test, risk factor data was used as input. The cross validation result shows an average accuracy of 87% in classifying ‘new’ samples. In the third test, both gene expression data and risk factor data were put together as one input. The cross validation procedure for this approach again indicates an average accuracy of 87% in classifying ‘new’ samples. In the final test, the C4.5 algorithm was used in order to indicate possible signalling pathways involving the four genes identified by the decision tree based on only gene expression data. In some of cases, the C4.5 algorithm found trees suggesting pathways which are supported by the breast cancer literature. Since not all pathways involving the four putative breast cancer genes are known yet, the other suggested pathways should be further analyzed in order to increase their credibility.</p><p>In summary, this study demonstrates the application of decision tree approaches for the identification of genes and risk factors relevant for the classification of breast cancer patients</p>
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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. Dimensionality reduction analysis andcluster analysis indicate that AC and SCC subtypes of NSCLC have different fibroblastcompositions. Differential gene expression analysis indicates that AC tumours have shownelevated content of MGP/PTGDS and INMT/MFAP4 fibroblast cells, whereas squamous cellcarcinoma showed an elevated content of COL6A1/COL6A2 and FNDC1/COL12A1 fibroblastcells. The statistical analysis shows that the clustering is statistically significant and not anartefact. Given that the tumour microenvironment is highly dynamic, in this study we haveattempted to understand the tumour microenvironment by scRNA analysis of non-immune cellsat single cell resolution.
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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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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 signal transduction in the prostate under normal and cancer conditions.
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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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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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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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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> In chapter 1, I review the current theories behind carcinogenesis, the molecular factors that regulate gene expression, and statistical methods for analyzing genomic data. In chapter 2, I describe an approach, termed oncomix, developed to identify oncogene candidates from expression data obtained from tumor and adjacent normal tissue. I apply oncomix to breast cancer expression data and identify an oncogene candidate, <i>CBX2</i>, whose expression is gained in a subset of breast tumors. <i>CBX2</i> is expressed at low levels in most normal adult tissue, and the CBX2 protein contains a drug-targetable chromodomain, both of which are desirable properties in a potential therapeutic target. We then provide the first experimental evidence that <i>CBX2</i> regulates the growth of breast cancer cells. In chapter 3, I develop a method for identifying nuclear hormone receptors whose expression is lost in endometrial cancers relative to normal tissue. I report, for the first time, that the loss of expression of Thyroid Hormone Receptor Beta (<i>THRB</i>) is associated with better 5-year survival in endometrial cancer. The loss of <i>THRB</i> expression is independent of the loss of estrogen and progesterone receptor expression, two genes whose loss of expression is known to be associated with poor survival. <i> THRB</i> expression could be considered as a biomarker to risk-stratify endometrial cancer patients. In Chapter 4, I develop a user-friendly application for visualizing chromosomal copy number state obtained from three types of copy number input in single cells – fluorescence in situ hybridization (FISH), spectral karyotyping (SKY), and whole genome sequencing (WGS). This web application, termed aneuvis, automatically creates novel visualizations and summary statistics from a set of user-uploaded files that contain chromosomal copy number information.</p><p> In this thesis, I develop new computational approaches for identifying candidate molecular regulators of cancer. I also develop a new user-friendly tool to enable biological researchers to identify aneuploidy and chromosomal instability within populations of single cells. Applying these tools to breast and endometrial cancer genomic datasets has highlighted novel aspects of breast and endometrial cancer biology and may inform novel therapeutic strategies based on molecular patterns of genomic heterogeneity. The freely available software developed as part of these projects has the potential to enable other researchers to advance our understanding of cancer genomics and to inform novel therapeutic strategies against cancer.</p><p>
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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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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 junction classification, gene regulatory network reconstruction, and lesion detection in mammograms.
A critical step in defining gene structures and mRNA transcript variants is to accurately identify splice junctions. In the first work, we built the first deep learning-based splice junction classifier, DeepSplice. It outperforms the state-of-the-art classification tools in terms of both classification accuracy and computational efficiency. To uncover transcription factors governing metabolic reprogramming in non-small-cell lung cancer patients, we developed TFmeta, a machine learning approach to reconstruct relationships between transcription factors and their target genes in the second work. Our approach achieves the best performance on benchmark data sets. In the third work, we designed deep learning-based architectures to perform lesion detection in both 2D and 3D whole mammogram images.
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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 prediction of survival. In this context, it is questioned, among other things, whether miRNA deregulations in cancer cells occur according to some pattern or in a rather random order. With this work we contribute to answering this question by adapting two approaches (Beerenwinkel et al. (J Comput Biol, 2005) and Höglund et al. (Gene Chromosome Canc, 2001)), developed to derive graph models of oncogenesis for chromosomal imbalances, to miRNA expression data and applying them to a breast cancer data set. Further, we evaluated the results by comparing them to results derived from randomly altered versions of the used data set.</p><p>We could show that miRNA deregulations most likely follow a rough temporal order, i.e. some deregulations occur early and some occur late in cancer progression. Thus, it seems to be possible that the expression level of some miRNAs can be used as indicator for the stage of a tumor. Further, our results suggest that the over expression of mir-21 as well as mir-102 are initial events in breast cancer oncogenesis.</p><p>Additionally, we identified a set of miRNAs showing a cluster-like behavior, i.e. their deregulations often occur together in a tumor, but other deregulations are less frequently present. These miRNAs are let-7d, mir-10b, mir-125a, mir-125b, mir-145, mir-206, and mir-210.</p><p>Further, we could confirm the strong relationship between the expression of mir-125a and mir-125b.</p>
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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 DNA fragmentation, interstrand DNA crosslinking and sequence artefacts due to hydrolytic deamination. Distinguishing such artefacts from true somatic variants can be challenging, thus affecting both research and clinical analyses. In order to identify FFPE-artefacts from true variants in next generation sequencing data from FFPE samples, I developed the novelprogram FUSAC (FFPE tissue UMI based Sequence Artefact Classifier) for the facility Clinical Genomics in Uppsala. FUSAC utilizes UniqueMolecular Identifiers (UMI's) to identify and group sequencing reads based on their molecule of origin. By using UMI's to collapse duplicate paired reads into consensus reads, FFPE-artefacts are classified through comparative analysis of the positive and negative strand sequences. My findings indicate that FUSAC can succesfully classify UMI-tagged next generation sequencing reads with FFPE-artefacts, from sequencing reads with true variants. FUSAC thus presents a novel approach in bioinformatic pipelines for studying FFPE-artefacts.
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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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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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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 set. The meta-analysis revealed that ER-/Grade 3 tumors show increased expression of proliferation-associated functional categories when compared to ER+/Grade 3 tumors. Using Gene Set Enrichment Analysis we show that transcript levels of direct transcriptional targets of ER are lower in ER- tumors, but that expression of other estrogen-induced genes is higher in ER- tumors. Transcript levels of both direct and other targets of the estrogen-regulated MYC gene and the E2F family of genes are significantly higher in ER- tumors. The increased expression of targets of MYC and E2F is particularly pronounced in the "basal" subgroup of ER- tumors. This suggests that a study assessing the association of these genes with clinical outcome in ER- patients is warranted, but is not currently feasible due to lack of suitable publicly available data. The contribution of genes regulated or bound by estrogen, MYC or E2F to increased risk of relapse in ER+ tamoxifen-treated patients was assessed in a pilot study using Cox proportional hazards models and Gene Set Enrichment Analysis. The high expression of several gene sets containing genes induced by estrogen and/or MYC and direct targets of MYC and E2F was correlated with poor outcome in these patients. We conclude that over-expression or constitutive activation of MYC, possibly in conjunction with elevated E2F activity, may lead to the induction of a set of genes characteristic of the estrogen response thereby contributing to increased proliferation in ER- breast tumors, particularly in the basal subgroup. A pilot survival study indicated that MYC- and E2F-activity may play a role in tamoxifen-resistance.
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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 patterns in correlation structure in several subsets of the data. We find that the datasets show consistency in their correlation structure, and in the specific miRNA-mRNA pairs that are either highly positively or negatively correlated. The data include a larger number of strong positive and strong negative correlations than would be expected by chance, indicating that biological relationships between the types of data are detectable in these datasets. We further find an enrichment for positively-correlated miRNA-mRNA pairs in which the miRNA is encoded in close proximity to the mRNA. The correlation of miRNA and mRNA is apparently unaffected by miRNA and mRNA expression level; similarly the two molecular subtypes do not contain differences in their correlation. We find that the recently described poorer prognosis, or angiogenic, subtype has a generally lower miRNA activity than the second, non-angiogenic, subtype. The subtypes are characterized by a consistent pattern of differential miRNA expression. We also report on a switch-like relationship between the expression levels of certain miRNAs and the genes that are anticorrelated with them. We propose these miRNAs drive many of the differences in the subtypes both directly, by RISC-mediated repression of target messages and indirectly, by repressing transcription factors that regulate expression in the cell. We build models of patient survival and time-to-relapse based on these miRNA expression data and inferred miRNA activity scores, using several types of univariate and variable selection models. We find essentially no survival-predictive information provided by the RE score data. While the direct miRNA expression measurements may contain some predictive power, we find that a larger dataset and the segretation of that dataset into distinct molecular phenotypes is likely to be necessary to produce a useful model of survival in ovarian cancer.
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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 "bystander" genes, in addition to vulnerabilities directly associated with the targets. </p><p> We approached the goal of understanding how the structure of SCNAs may lead to dependency in two ways. To improve our understanding of how SCNAs promote tumor progression we analyzed the SCNAs in 4934 primary tumors in 11 common cancers collected by the Cancer Genome Atlas (TCGA). The scale of this dataset provided insights into the structure and patterns of SCNA, including purity and ploidy rates across disease, mechanistic forces shaping patterns of SCNA, regions undergoing significantly recurrent SCNAs, and correlations between SCNAs in regions implicated in cancer formation. </p><p> In a complementary approach, we integrating SCNA data and pooled RNAi screening data involving 11,000 genes across 86 cell lines to find non-driver genes whose partial loss led to increased sensitivity to RNAi suppression. We identified a new set of cancer specific vulnerabilities predicted by loss of non-driver genes, with the most significant gene being PSMC2, an obligate member of the 26S proteasome. Biochemically, we found that PSMC2 is in excess of cellular requirement in diploid cells, but becomes the stoichiometric limiting factor in proteasome formation after partial loss of this gene. </p><p> In summary, my work improved our understanding of the structure and patterns of SCNA, both informing how cancers develop and predicting novel cancer vulnerabilities. Our characterization of the SCNAs present across 5000 tumors uncovered novel structure in SCNAs and significant regions likely to contain driver genes. Through integrating SCNA data with the results of a functional genetic screen, we also uncovered a new set of vulnerabilities caused by unintended loss of non-driver genes.</p>
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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 important problem in bioinformatics is the question of how the inferred networks’ quality can be evaluated. The current best practice is a two step procedure. In the first step, the highest scoring interactions are compared to known interactions stored in biological databases. The inferred networks passes this quality assessment if there is a large overlap with the known interactions. In this case, a second step is carried out in which unknown but high scoring and thus promising new interactions are validated ’by hand’ via laboratory experiments. Unfortunately when integrating prior knowledge in the inference procedure, this validation procedure would be biased by using the same information in both the inference and the validation. Therefore, it would no longer allow an independent validation of the resulting network.<p><p>The main contribution of this thesis is a complete computational framework that uses experimental knock down data in a cross-validation scheme to both infer and validate directed networks. Its components are i) a method that integrates genomic data and prior knowledge to infer directed networks, ii) its implementation in an R/Bioconductor package and iii) a web application to retrieve prior knowledge from PubMed abstracts and biological databases. To infer directed networks from genomic data and prior knowledge, we propose a two step procedure: First, we adapt the pairwise feature selection strategy mRMR to integrate prior knowledge in order to obtain the network’s skeleton. Then for the subsequent orientation phase of the algorithm, we extend a criterion based on interaction information to include prior knowledge. The implementation of this method is available both as part of the prior retrieval tool Predictive Networks and as a stand-alone R/Bioconductor package named predictionet.<p><p>Furthermore, we propose a fully data-driven quantitative validation of such directed networks using experimental knock-down data: We start by identifying the set of genes that was truly affected by the perturbation experiment. The rationale of our validation procedure is that these truly affected genes should also be part of the perturbed gene’s childhood in the inferred network. Consequently, we can compute a performance score<br>Doctorat en Sciences<br>info:eu-repo/semantics/nonPublished
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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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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 and duplications, together with efficient computational methods for various inference problems. Specifically, we define a biologically sound combinatorial model for reconciliation of species and gene trees that facilitates simultaneous consideration of duplications and LGTs. We prove that finding most parsimonious reconciliations is NP-hard, but that the problem can be solved efficiently if reconciliations are not required to be acyclic—a condition that is satisfied when analyzing most real-world datasets. We also provide a polynomial-time algorithm for parametric tree reconciliation, a problem analogous to parametric sequence alignment, that enables us to study the entire space of optimal reconciliations under all possible cost schemes. Going beyond combinatorial models, we define the first probabilistic model of gene evolution incorporating a birth-death process generating duplications, LGTs, and losses, together with a relaxed molecular clock model of sequence evolution. Algorithms based on Markov chain Monte Carlo (MCMC) techniques, methods from numerical analysis, and dynamic programming are presented for various probability and parameter inference problems. Finally, we develop methods for analysis of cancer progression, a biological process with many similarities to the process of evolution. Cancer progresses by accumulation of harmful genetic aberrations whose patterns of emergence are graph-like. We develop a model of cancer progression based on trees, and mixtures thereof, that admits an efficient structural EM algorithm for finding Maximum Likelihood (ML) solutions from available cross-sectional data.<br>QC 20100812
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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 new ensemble prognostic model to perform risk prediction for mCRPC patients treated with docetaxel. We rely on traditional survival analysis model like the Cox Proportional Hazard model, as well as more recently developed boosting model that incorporates smooth approximation of the concordance index for direct optimization. Our model performs better than the the current state-of-the-art mCRPC prognostic models for the concordance index performance measure and is competitive with these models on the integrated time-dependent area under the receiver operating characteristic curve.</p>
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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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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 experimental studies and published literature revealed 72.8 % correlation between mRNA and sphingolipid differences (p-value < 0.0001 by the Fisher's exact test).The high correlation between gene expression differences and sphingolipid alterations highlights the application of this tool to evaluate molecular changes associate with sphingolipid alterations as well as predict differences in specific metabolites that can be experimentally verified using sensitive approaches such as mass spectrometry. In addition, bioinformatics sequence analysis was used to identify transcripts for sphingolipid biosynthesis enzyme 3-ketosphinganine reductase, and homology modeling studies helped in the evaluation of a cell line defective in sphingolipid metabolism due to mutation in the enzyme serine palmitoyltransferase, the first enzyme of de novo biosynthesis pathway. Hence, the combination of different bioinformatics approaches, including protein and DNA sequence analysis, structure modeling and pathway diagrams can provide valuable inputs for biochemical and molecular studies of sphingolipid metabolism.
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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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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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