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Books on the topic 'Genomic biomarker'

Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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1

Eric, Blomme, ed. Genomics in drug discovery and development. Hoboken, N.J: John Wiley, 2008.

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2

Semizarov, Dimitri. Genomics in drug discovery and development. Hoboken, N.J: Wiley, 2009.

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3

Zhang, Xuewu. Omics technologies in cancer biomarker discovery. Austin, Tex: Landes Bioscience, 2011.

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4

Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.

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5

Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.

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6

Azuaje, Francisco. Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.

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7

Bioinformatics and biomarker discovery: "omic" data analysis for personalised medicine. Hoboken, NJ: John Wiley & Sons, 2010.

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8

International Conference on Toxic Exposure Related Biomarker, Genomes, and Health Effects (2008 National Environmental Engineering Research Institute). International Conference on Toxic Exposure Related Biomarker, Genomes, and Health Effects: Under the aegis of NEERI's golden jubilee celebrations, 2007-2008, 10-11 January 2008. Nagpur: National Environmental Engineering Research Institute, 2008.

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9

Genomic Biomarkers for Pharmaceutical Development. Elsevier, 2014. http://dx.doi.org/10.1016/c2011-0-08165-6.

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10

Haghighi, Afshin Borhani, and Bernadette Kalman. Other Proven and Putative Autoimmune Disorders of the CNS. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0094.

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Behcet’s Disease (BD) is a multiorgan disorder characterized by oral and genital ulceration, uveitis, and dermatological symptoms. BD is most prevalent in the Mediterranean countries and East Asia, but also occurs in Europe and North America. The etiology remains unknown. Evidence suggests that BD is an autoimmune disorder with complex traits. Neuro-Behcet’s Syndome (NBS) develops in about 5% to 30% of patients with BD and presents with parenchymal or nonparenchymal pathology. The course of NBS is highly variable. Treatment strategies include modulations of the immune response and tissue degeneration, along with symptomatic medications. Main directions of current research include genomic studies, biomarker discovery, and inventive drug- development strategies.

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11

Sahu, Saura C., ed. Genomic and Epigenomic Biomarkers of Toxicology and Disease. Wiley, 2022. http://dx.doi.org/10.1002/9781119807704.

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12

Azuaje, Francisco. Bioinformatics and Biomarker Discovery. Wiley & Sons, Incorporated, John, 2009.

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13

Genomic Biomarkers for Pharmaceutical Development: Advancing Personalized Health Care. Elsevier Science & Technology Books, 2013.

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14

Yao, Yihong, Bahija Jallal, and Koustubh Ranade. Genomic Biomarkers for Pharmaceutical Development: Advancing Personalized Health Care. Elsevier Science & Technology Books, 2013.

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15

Casanova, Nancy G., Ting Wang, Eddie T. Chiang, and Joe G. N. Garcia. Genomics, Epigenetics, and Precision Medicine in Integrative Preventive Medicine. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190241254.003.0004.

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This chapter briefly reviews the use of genomewide screening for early detection, treatment, and prevention and the utility of genome-based biomarkers as a tool for precision medicine and its application to population and integrative preventive medicine. Advances in technology have made genomic screening more affordable and widely available, and both our understanding and the value of testing grow as more data is collected. Even more recently, the growing availability of epigenetic testing, methylation and ROS-associated molecular signatures are providing more insight into dynamic aspects of the human genome and how lifestyle and IPM change affect the expression of the genome. Early adoption of precision medicine in oncology offers a model that should be expanded into wider areas of treatment and prevention.

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16

Semizarov, Dimitri, and Eric Blomme. Genomics in Drug Discovery and Development. Wiley & Sons, Incorporated, John, 2008.

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17

Semizarov, Dimitri, and Eric Blomme. Genomics in Drug Discovery and Development. Wiley & Sons, Incorporated, John, 2008.

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18

Semizarov, Dimitri, and Eric Blomme. Genomics in Drug Discovery and Development. Wiley & Sons, Limited, John, 2008.

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19

Sahu, Saura C. Genomic and Epigenomic Biomarkers of Toxicology and Disease: Clinical and Therapeutic Actions. Wiley & Sons, Limited, John, 2022.

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20

Sahu, Saura C. Genomic and Epigenomic Biomarkers of Toxicology and Disease: Clinical and Therapeutic Actions. Wiley & Sons, Limited, John, 2022.

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21

Sahu, Saura C. Genomic and Epigenomic Biomarkers of Toxicology and Disease: Clinical and Therapeutic Actions. Wiley & Sons, Incorporated, John, 2022.

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22

Sahu, Saura C. Genomic and Epigenomic Biomarkers of Toxicology and Disease: Clinical and Therapeutic Actions. Wiley & Sons, Incorporated, John, 2022.

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23

Vermeulen, Roel, Douglas A. Bell, Dean P. Jones, Montserrat Garcia-Closas, Avrum Spira, Teresa W. Wang, Martyn T. Smith, Qing Lan, and Nathaniel Rothman. Application of Biomarkers in Cancer Epidemiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0006.

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Advancements in OMICs are now enabling investigators to explore comprehensively the biological consequences of exogenous and endogenous exposures by detecting molecular signatures of exposure, early signs of adverse biological effects, preclinical disease, and molecularly defined cancer subtypes. These new technologies have proven invaluable for assembling a comprehensive portrait of human exposure, health, and disease. This includes hypothesis-driven biomarkers, as well as platforms that can agnostically analyze entire biologic processes and “compartments,” including the measurement of small molecules (metabolomics), DNA polymorphisms and rarer inherited variants (genomics), methylation and microRNA (epigenomics), chromosome-wide alterations, mRNA (transcriptomics), proteins (proteomics), and the microbiome (microbiomics). Although the implementation of these technologies in epidemiologic studies has already shown great promise, some challenges of particular importance must be addressed. Non-genetic OMIC markers vary over time due to both random variation and physiologic changes. Therefore, there is an urgent need for cohorts to collect repeat biological samples over time.

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24

Merl, Dan, Joseph Lucas, Joseph Nevins, Haige Shen, and Mike West. Trans-study projection of genomic biomarkers in analysis of oncogene deregulation and breast cancer. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.6.

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This article focuses on the use of Bayesian concepts and methods in the trans-study projection of genomic biomarkers for the analysis of oncogene deregulation in breast cancer. The objective of the study is to determine the extent to which patterns of gene expression associated with experimentally induced oncogene pathway deregulation can be used to investigate oncogene pathway activity in real human cancers. This is often referred to as the in vitro to in vivo translation problem, which is addressed using Bayesian sparse factor regression analysis for model-based translation and refinement of in vitro generated signatures of oncogene pathway activity into the domain of human breast tumour tissue samples. The article first provides an overview of the role of oncogene pathway deregulation in human cancers before discussing the details of modelling and data analysis. It then considers the findings based on biological evaluation and Bayesian pathway annotation analysis.

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25

Azuaje, Francisco. Bioinformatics and Biomarker Discovery: Omic Data Analysis for Personalized Medicine. Wiley & Sons, Limited, John, 2010.

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26

Azuaje, Francisco. Bioinformatics and Biomarker Discovery: Omic Data Analysis for Personalized Medicine. Wiley & Sons, Incorporated, John, 2009.

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27

Azuaje, Francisco. Bioinformatics and Biomarker Discovery: Omic Data Analysis for Personalized Medicine. Wiley & Sons, Incorporated, John, 2011.

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28

Azuaje, Francisco. Bioinformatics and Biomarker Discovery: Omic Data Analysis for Personalized Medicine. Wiley & Sons, Incorporated, John, 2011.

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29

Ali-Fehmi, Rouba, and Eman Abdulfatah. Biological Aspects and Clinical Applications of Serum Biomarkers in Ovarian Cancer. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190248208.003.0002.

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Ovarian cancer, the most aggressive gynecological malignancy, presents at advanced stages with metastatic disease. Diagnosis at an early stage is the most important determinant of survival; however, the majority of patients are asymptomatic at early stages and the current diagnostic tools used in clinics show limited success in early detection and hence the need for new diagnostic biomarkers. With the advance of techniques in genomic and proteomics, numerous biomarkers are emerging which may serve as a platform for early detection of ovarian cancer. These include gene-, protein-, miRNAs, and metabolite- based biomarkers. Examples of gene-based biomarkers include HE4, FLOR1, p16INK4a, BRCA1, BRCA2, MLH1, and MSH2. Protein- based biomarkers include leptin, prolactin, osteopontin, IGF-II, and MIF. This chapter discusses the serum tumor markers (CA-125) in current use for screening, diagnosis and monitoring of ovarian cancer as well as the novel biomarkers that are under investigation and validation.

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30

Ritsner, Michael S. The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes : Volume IV: Molecular Genetic and Genomic Markers. Springer, 2010.

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31

Thun, Michael J., Martha S. Linet, James R. Cerhan, Christopher A. Haiman, and David Schottenfeld. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0001.

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This Introduction provides a broad overview of the scientific advances and crosscutting developments that increasingly influence epidemiologic research on the causes and prevention of cancer. High-throughput technologies have identified the molecular “driver” events in tumor tissue that underlie the multistage development of many types of cancer. These somatic (largely acquired) alterations disrupt normal genetic and epigenetic control over cell maintenance, division and survival. Tumor classification is also changing to reflect the genetic and molecular alterations in tumor tissue, as well as the anatomic, morphologic, and histologic phenotype of the cancer. Genome-wide association studies (GWAS) have identified more than 700 germline (inherited) genetic loci associated with susceptibility to various forms of cancer, although the risk estimates for almost all of these are small to modest and their exact location and function remain to identified. Advances in genomic and other “OMIC” technologies are identifying biomarkers that reflect internal exposures, biological processes and intermediate outcomes in large population studies. While research in many of these areas is still in its infancy, mechanistic and molecular assays are increasingly incorporated into etiologic studies and inferences about causation. Other sections of the book discuss the global public health impact of cancer, the growing list of exposures known to affect cancer risk, the epidemiology of over 30 types of cancer by tissue of origin, and preventive interventions that have dramatically reduced the incidence rates of several major cancers.

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32

Barnes, Rosemary A., and Matthijs Backx. Fungal infections in intensive therapy units. Edited by Christopher C. Kibbler, Richard Barton, Neil A. R. Gow, Susan Howell, Donna M. MacCallum, and Rohini J. Manuel. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198755388.003.0036.

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Invasive candidiasis remains the main cause of invasive fungal disease in the intensive care unit. The risk of infection is often overestimated and most units will have incidences of 1–2% or lower. Units with higher incidences may have specific geographical and epidemiological factors, or may need to address infection control issues contributing to transmission. Routine use of prophylaxis or empiric therapy is not warranted at this level of disease. Discriminatory risk factors for this low incidence of disease are poorly defined and Candida specific biomarkers have not been validated for pre-emptive therapy. Insights into human response to invasive fungal disease gained from proteomic and genomic studies will increase our understanding, enabling us to target fungal diagnostics and antifungal treatments more accurately.

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33

Haiman, Christopher, and David J. Hunter. Genetic Epidemiology of Cancer. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190676827.003.0004.

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This chapter explores the genetic epidemiology of cancer: the identification and quantification of inherited genetic factors, and their potential interaction with the environment, in the etiology of cancer in human populations. It also describes the techniques used to identify genetic variants that contribute to cancer susceptibility. It describes the older research methods for identifying the chromosomal localization of high-risk predisposing genes, such as linkage analysis within pedigrees and allele-sharing methods, as it is important to understand the foundations of the field. It also reviews the epidemiologic study designs that can be helpful in identifying low-risk alleles in candidate gene and genome-wide association studies, as well as gene–environment interactions. Finally, it describes some of the genotyping and sequencing platforms commonly employed for high-throughput genome analysis, and the concept of Mendelian randomization and how it may be useful in the study of biomarkers and environmental causes of cancer.

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34

Guffanti, Guia, Milissa L. Kaufman, Lauren A. M. Lebois, and Kerry J. Ressler. Genetic Approaches to Post-Traumatic Stress Disorder. Edited by Charles B. Nemeroff and Charles R. Marmar. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190259440.003.0026.

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Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder with an estimated genetic component accounting for 30%–40% of the variance contributing to risk for the disease. This chapter starts with a review of the biological hypotheses and related genetic mechanisms currently proposed to be associated with PTSD and trauma-related disorders. It will follow with a description of the state-of-the-art on the methodologies and their application to map genetic loci and identify biomarkers associated with PTSD. Finally, we will review the latest results from genome-wide association studies of genetic variants as well as those derived from the emerging fields of epigenetics and gene expression.

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