Thematische Bibliographien / Cancer bioinformatics

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Auswahl der wissenschaftlichen Literatur zum Thema „Cancer bioinformatics“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 13. Februar 2022

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Zeitschriftenartikel zum Thema "Cancer bioinformatics"

Desany, Brian, und Zemin Zhang. „Bioinformatics and cancer target discovery“. Drug Discovery Today 9, Nr. 18 (September 2004): 795–802. http://dx.doi.org/10.1016/s1359-6446(04)03224-6.

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Brenner, Chad. „Applications of Bioinformatics in Cancer“. Cancers 11, Nr. 11 (24.10.2019): 1630. http://dx.doi.org/10.3390/cancers11111630.

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Blekherman, Grigoriy, Reinhard Laubenbacher, Diego F. Cortes, Pedro Mendes, Frank M. Torti, Steven Akman, Suzy V. Torti und Vladimir Shulaev. „Bioinformatics tools for cancer metabolomics“. Metabolomics 7, Nr. 3 (12.01.2011): 329–43. http://dx.doi.org/10.1007/s11306-010-0270-3.

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Puig, Oscar, Eugene Joseph, Malgorzata Jaremko, Gregory Kellogg, Robert Wisotzkey, Roman Shraga, Bonny Patel et al. „Comprehensive next generation sequencing assay and bioinformatic pipeline for identifying pathogenic variants associated with hereditary cancers.“ Journal of Clinical Oncology 35, Nr. 15_suppl (20.05.2017): e13105-e13105. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e13105.

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e13105 Background: Diagnosis of hereditary cancer syndromes involves time-consuming comprehensive clinical and laboratory work-up, however, timely and accurate diagnosis is pivotal to the clinical management of cancer patients. Germline genetic testing has shown to facilitate the diagnostic process, allowing for identification and management of individuals at risk for inherited cancers. However, the laboratory diagnostics process requires not only development and validation of comprehensive gene panels to improve diagnostic yields, but a quality driven workflow including an end-to-end bioinformatics pipeline, and a robust process for variant classification. We will present a gene panel for the evaluation of hereditary cancer syndromes, conducted utilizing our novel end-to-end workflow, and validated in the CLIA-approved environment. Methods: A targeted Next-Generation Sequencing (NGS) panel consisting of 130 genes, including exons, promoters, 5’-UTRs, 3’-UTRs and selected introns, was designed to include genes associated with hereditary cancers. The assay was validated using samples from the 1000 genomes project and samples with known pathogenic variants. Elements software was utilized for end-to-end bioinformatic process ensuring adherence with the CLIA quality standards, and supporting manual curation of sequence variants. Results: Preliminary data from our current panel of genes associated with hereditary cancer syndromes revealed high sensitivity, specificity, and positive predictive value. Accuracy was confirmed by analysis of known SNVs, indels, and CNVs using 1000 Genomes and samples carrying pathogenic variants. The bioinformatics software allowed for an end-to-end quality controlled process of handling and analyzing of the NGS data, showing applicability for a clinical laboratory workflow. Conclusions: We have developed a comprehensive and accurate genetic testing process based on an automated and quality driven bioinformatics workflow that can be used to identify clinically important variants in genes associated with hereditary cancers. It's performance allows for implementation in the clinical laboratory setting.

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UMAR, ASAD. „Applications of Bioinformatics in Cancer Detection: A Lexicon of Bioinformatics Terms“. Annals of the New York Academy of Sciences 1020, Nr. 1 (Mai 2004): 263–76. http://dx.doi.org/10.1196/annals.1310.021.

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Fenstermacher, David A. „Book Review: Bioinformatics in Cancer and Cancer Therapy“. Cancer Control 16, Nr. 4 (Oktober 2009): 349. http://dx.doi.org/10.1177/107327480901600411.

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Xu, Chaobo, und Ming Liu. „Integrative bioinformatics analysis of KPNA2 in six major human cancers“. Open Medicine 16, Nr. 1 (01.01.2021): 498–511. http://dx.doi.org/10.1515/med-2021-0257.

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Abstract Background Malignant tumors were considered as the leading causes of cancer-related mortality globally. More and more studies found that dysregulated genes played an important role in carcinogenesis. The aim of this study was to explore the significance of KPNA2 in human six major cancers including non-small cell lung cancer (NSCLC), gastric cancer, colorectal cancer, breast cancer, hepatocellular carcinoma, and bladder cancer based on bioinformatics analysis. Methods The data were collected and comprehensively analyzed based on multiple databases. KPNA2 mRNA expression in six major cancers was investigated in Oncomine, the human protein atlas, and GEPIA databases. The mutation status of KPNA2 in the six major cancers was evaluated by online data analysis tool Catalog of Somatic Mutations in Cancer (COSMIC) and cBioPortal. Co-expressed genes with KPNA2 were identified by using LinkedOmics and made pairwise correlation by Cancer Regulome tools. Protein-protein interaction (PPI) network relevant to KPNA2 was constructed by STRING database and KEGG pathway of the included proteins of the PPI network was explored and demonstrated by circus plot. Survival analysis-relevant KPNA2 of the six cancers was performed by GEPIA online data analysis tool based on TCGA database. Results Compared with paired normal tissue, KPNA2 mRNA was upregulated in all of the six types of cancers. KPNA2 mutations, especially missense substitution, were widely identified in six cancers and interact with different genes in different cancer types. Genes involved in PPI network were mainly enriched in p53 signaling pathway, cell cycle, viral carcinogenesis, and Foxo signaling pathway. KPNA2 protein was mainly expressed in nucleoplasm and cytosol in cancer cells. Immunohistochemistry assay indicated that KPNA2 protein was also positively expressed in nucleoplasm with brownish yellow staining. Overall survival (OS) and progression free survival (PFS) were different between KPNA2 high and low expression groups. Conclusions KPNA2 was widely dysregulated and mutated in carcinomas and correlated with the patients prognosis which may be potential target for cancer treatment and biomarker for prognosis.

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Van Neste, Leander, James G. Herman, Kornel E. Schuebel, Leslie Cope, Stephen B. Baylin, Wim Van Criekinge und Nita Ahuja. „A Bioinformatics Pipeline for Cancer Epigenetics“. Current Bioinformatics 5, Nr. 3 (01.09.2010): 153–63. http://dx.doi.org/10.2174/157489310792006710.

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YANG, HOWARD H., und MAXWELL P. LEE. „Application of Bioinformatics in Cancer Epigenetics“. Annals of the New York Academy of Sciences 1020, Nr. 1 (Mai 2004): 67–76. http://dx.doi.org/10.1196/annals.1310.008.

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Charoentong, Pornpimol, Mihaela Angelova, Mirjana Efremova, Ralf Gallasch, Hubert Hackl, Jerome Galon und Zlatko Trajanoski. „Bioinformatics for cancer immunology and immunotherapy“. Cancer Immunology, Immunotherapy 61, Nr. 11 (18.09.2012): 1885–903. http://dx.doi.org/10.1007/s00262-012-1354-x.

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Dissertationen zum Thema "Cancer bioinformatics"

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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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.

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.

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.

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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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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.

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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.
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.
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.
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.
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.
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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Bücher zum Thema "Cancer bioinformatics"

Nagl, Sylvia, Hrsg. Cancer Bioinformatics. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470032898.

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Krasnitz, Alexander, Hrsg. Cancer Bioinformatics. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8868-6.

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Xu, Ying, Juan Cui und David Puett. Cancer Bioinformatics. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1381-7.

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Gordon, Gavin J., Hrsg. Bioinformatics in Cancer and Cancer Therapy. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-576-3.

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Boegel, Sebastian, Hrsg. Bioinformatics for Cancer Immunotherapy. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0327-7.

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Cesario, Alfredo, und Frederick Marcus, Hrsg. Cancer Systems Biology, Bioinformatics and Medicine. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1567-7.

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Schmitz, Ulf. MicroRNA Cancer Regulation: Advanced Concepts, Bioinformatics and Systems Biology Tools. Dordrecht: Springer Netherlands, 2013.

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Cesario, Alfredo. Cancer Systems Biology, Bioinformatics and Medicine: Research and Clinical Applications. Dordrecht: Springer Science+Business Media B.V., 2011.

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Computational systems biology of cancer. Boca Raton, FL: Taylor & Francis, 2013.

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Kashyap, Amita, D. Bujamma und Naresh Babu M. Bioinformatics of Non Small Cell Lung Cancer and the Ras Proto-Oncogene. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-4585-08-8.

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Buchteile zum Thema "Cancer bioinformatics"

He, Mingyan, Li Feng und Jinglin Xia. „Cancer Bioinformatics“. In Single Cell Sequencing and Systems Immunology, 175–77. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9753-5_13.

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Meetz, Kirsten, Hans-Peter Meinzer, Sândor Suhai und Martina Kieninger. „Bioinformatics“. In Current Cancer Research 1992, 183–200. Heidelberg: Steinkopff, 1992. http://dx.doi.org/10.1007/978-3-662-11384-4_10.

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Reczko, Martin, Sándor Suhai, Annemarie Poustka, Uwe Engelmann, Manuela Schäfer und Hans-Peter Meinzer. „Bioinformatics“. In Current Cancer Research 1995, 147–66. Heidelberg: Steinkopff, 1995. http://dx.doi.org/10.1007/978-3-642-48687-6_11.

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Neidle, Stephen. „Structural Bioinformatics in Cancer“. In Cancer Bioinformatics, 127–40. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470032898.ch7.

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Xu, Ying, Juan Cui und David Puett. „Basic Cancer Biology“. In Cancer Bioinformatics, 1–39. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1381-7_1.

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Nagl, Sylvia. „A Path to Knowledge: from Data to Complex Systems Models of Cancer“. In Cancer Bioinformatics, 1–27. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470032898.ch1.

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Brentani, Ricardo, Anamaria A. Camargo, Helena Brentani und Sandro J. De Souza. „The FAPESP/LICR Human Cancer Genome Project: Perspectives on Integration“. In Cancer Bioinformatics, 169–84. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470032898.ch10.

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Knox, Kirstine, Amanda Taylor und David J. Kerr. „Today's Science, Tomorrow's Patient: the Pivotal Role of Tissue, Clinical Data and Informatics in Modern Drug Development“. In Cancer Bioinformatics, 185–209. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470032898.ch11.

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Gotterbarn, Don, und Simon Rogerson. „Software Design Ethics for Biomedicine“. In Cancer Bioinformatics, 211–31. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470032898.ch12.

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Kalra, Dipak, und David Ingram. „Ethical Issues of Electronic Patient Data and Informatics in Clinical Trial Settings“. In Cancer Bioinformatics, 233–56. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470032898.ch13.

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Konferenzberichte zum Thema "Cancer bioinformatics"

Giakos, George C., Stefanie Marotta, Suman Shrestha, Aditi Deshpande, Tannaz Farrahi, Lin Zhang, Thomas Cambria et al. „Bioinformatics of Lung Cancer“. In 2015 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2015. http://dx.doi.org/10.1109/ist.2015.7294524.

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Zhang, Jingshu, Hao Sun, Xuyao An, Kun Yu, Penglin Li und Hao Sun. „Bioinformatics analysis of colorectal cancer related gene“. In 2019 6th International Conference on Systems and Informatics (ICSAI). IEEE, 2019. http://dx.doi.org/10.1109/icsai48974.2019.9010480.

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Hashem, Hasan, und Iyad Sultan. „Immune Dysregulation Disorders in the Bioinformatics Paradigm“. In 2018 1st International Conference on Cancer Care Informatics (CCI). IEEE, 2018. http://dx.doi.org/10.1109/cancercare.2018.8618246.

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Goldin, Leah. „Bioinformatics Integration for Cancer Research-Goal Question analysis“. In 2006 International Conference on Information Technology: Research and Education. IEEE, 2006. http://dx.doi.org/10.1109/itre.2006.381575.

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Liu, Wenjia, Nanjiao Ying, Qiusi Mo und Lei Zhu. „Screening Potential Biomarkers of Breast Cancer Based on Bioinformatics“. In ICBBS '20: 2020 9th International Conference on Bioinformatics and Biomedical Science. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3431943.3432282.

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Alsmadi, Osama, Mohammed Odeh, Iyad Sultan, Anas Al-okaily und Abdelghani Tbakhi. „Bridging Arabian Mendelian and Complex Diseases Necessitates Utilizing Modern Bioinformatics“. In 2018 1st International Conference on Cancer Care Informatics (CCI). IEEE, 2018. http://dx.doi.org/10.1109/cancercare.2018.8618237.

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Phan, J. H., Qiqin Yin-Goen, A. N. Young und M. D. Wang. „Emerging translational bioinformatics: Knowledge-guided biomarker identification for cancer diagnostics“. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5333937.

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Rodríguez-Segura, M. A., J. J. Godina-Nava und S. Villa-Treviño. „The bioinformatics of microarrays to study cancer: Advantages and disadvantages“. In MEDICAL PHYSICS: Twelfth Mexican Symposium on Medical Physics. AIP, 2012. http://dx.doi.org/10.1063/1.4764632.

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Wasti, Afshan Zeeshan. „ERBB2 - A Potential Breast Cancer Marker: An Integrated Bioinformatics Strategy“. In IBRAS 2021 INTERNATIONAL CONFERENCE ON BIOLOGICAL RESEARCH AND APPLIED SCIENCE. Juw, 2021. http://dx.doi.org/10.37962/ibras/2021/1-2.

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„PROGNOSIS OF BREAST CANCER BASED ON A FUZZY CLASSIFICATION METHOD“. In International Conference on Bioinformatics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002716601230130.

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Berichte der Organisationen zum Thema "Cancer bioinformatics"

Brueggemeier, Robert W. Drug Discovery and Structural Bioinformatics in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, Dezember 1999. http://dx.doi.org/10.21236/ada384146.

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