Livros: "Genomic classification"

1

Fenaux, Robert. The classification of Appendicularia (Tunicata): History and current state. [Monaco: Institut océanographique], 1993.

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2

Genome clustering: From linguistic models to classification of genetic texts. Berlin: Springer, 2010.

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3

Maes, Dominiek, Marina Sibila e Maria Pieters, eds. Mycoplasmas in swine. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249941.0000.

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Abstract This book contains 14 chapters that discuss the genetics, epidemiology, prevalence, pathogenesis, clinical signs, diagnosis, treatment, prevention and control of Mycoplasma infections in pigs. Chapter 1 discusses the phylogenetics and classification of Mycoplasma species in pigs; Chapter 2 describes the genomic diversity and antigenic variation of Mycoplasma hyopneumoniae strains; Chapter 3 discusses the pathogenesis, virulence factor and pathogenicity of Mycoplasma hyopneumoniae; Chapter 4 discusses the molecular epidemiology, risk factors, transmission and prevalence of Mycoplasma hyopneumoniae, Chapter 5 discusses the clinical signs and gross lesions of Mycoplasma hyopneumoniae infection; Chapter 6 discusses immune responses against Mycoplasma infections; Chapter 7 describes the interactions of Mycoplasma hyopneumoniae with other pathogens and their economic impact; Chapter 8 discusses the diagnosis of Mycoplasma hyopneumoniae infection and its associated diseases; Chapter 9 describes the general control measures against Mycoplasma hyopneumoniae infections; Chapter 10 describes the selection and efficacy of antimicrobials against Mycoplasma hyopneumoniae infections; Chapter 11 discusses the development and efficacy of vaccines against Mycoplasma hyopneumoniae; Chapter 12 describes the eradication of Mycoplasma hyopneumoniae in pig herds; Chapter 13 describes the epidemiology, prevalence, pathogenesis, clinical signs, diagnosis, treatment, prevention and control of Mycoplasma hyorhinis and Mycoplasma hyosynoviae in pig herds and Chapter 14 discusses the epidemiology, prevalence, transmission, pathogenesis, clinical signs, diagnosis, treatment, prevention, control and economic impact of Mycoplasma suis infection in pigs.

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4

1963-, Feng Zhi, e Long Ming, eds. Viral genomes: Diversity, properties, and parameters. Hauppauge, NY: Nova Science Publishers, 2009.

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5

Viruses and the environment. 2^a ed. London ; New York: Chapman and Hall, 1995.

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6

1954-, Heiner Monika, e SpringerLink (Online service), eds. Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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7

János, Varga, e Samson Robert A, eds. Aspergillus in the genomic era. Wageningen, Netherlands: Wageningen Academic Publishers, 2008.

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8

Matsui, Shigeyuki, e Hisashi Noma. Estimation and Selection in High-Dimensional Genomic Studies: Multiple Testing, Gene Ranking, and Classification. Springer, 2020.

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9

Sherman, Mark E., Melissa A. Troester, Katherine A. Hoadley e William F. Anderson. Morphological and Molecular Classification of Human Cancer. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0003.

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Accurate and reproducible classification of tumors is essential for clinical management, cancer surveillance, and studies of pathogenesis and etiology. Tumor classification has historically been based on the primary anatomic site or organ in which the tumor occurs and on its morphologic and histologic phenotype. While pathologic criteria are useful in predicting the average behavior of a group of tumors, histopathology alone cannot accurately predict the prognosis and treatment response of individual cancers. Traditional measures such as tumor stage and grade do not take into account molecular events that influence tumor aggressiveness or changes in the tumor composition during treatment. This chapter provides a primer on approaches that use pathology and molecular biology to classify and subclassify cancers. It describes the features of carcinomas, sarcomas, and malignant neoplasms of the immune system and blood, as well as various high-throughput genomic platforms that characterize the molecular profile of tumors.

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10

Bolshoy, Alexander, Zeev Volkovich e Valery Kirzhner. Genome Clustering: From Linguistic Models to Classification of Genetic Texts. Springer, 2010.

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11

Bolshoy, Alexander, Zeev Volkovich, Valery Kirzhner e Zeev Barzily. Genome Clustering: From Linguistic Models to Classification of Genetic Texts. Springer, 2012.

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12

(Editor), R. DeSalle, G. Giribet (Editor) e W. Wheeler (Editor), eds. Molecular Systematics and Evolution: Theory and Practice (Experientia Supplementum). Birkhauser, 2002.

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13

Pfeffer, Ulrich. Cancer Genomics: Molecular Classification, Prognosis and Response Prediction. Springer Netherlands, 2015.

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14

Pfeffer, Ulrich. Cancer Genomics: Molecular Classification, Prognosis and Response Prediction. Springer, 2013.

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15

Pfeffer, Ulrich. Cancer Genomics: Molecular Classification, Prognosis and Response Prediction. Springer London, Limited, 2013.

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16

Pfeffer, Ulrich. Cancer Genomics: Molecular Classification, Prognosis and Response Prediction. Springer, 2013.

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17

Thun, Michael J., Martha S. Linet, James R. Cerhan, Christopher A. Haiman e 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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18

Ellison, Aaron M., e Lubomír Adamec. The future of research with carnivorous plants. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779841.003.0029.

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The material presented in the chapters of Carnivorous Plants: Physiology, Ecology, and Evolution together provide a suite of common themes that could provide a framework for increasing progress in understanding carnivorous plants. All speciose genera would benefit from more robust, intra-generic classifications in a phylogenetic framework that uses a unified species concept. As more genomic, proteomic, and transcriptomic data accrue, new insights will emerge regarding trap biochemistry and regulation; interactions with commensals; and the importance of intraspecific variability on which natural selection works. Continued elaboration of field experiments will provide new insights into basic physiology; population biology; plant-animal and plant-microbe relationships; and evolutionary dynamics, all of which will aid conservation efforts and contribute to discussions of assisted migration as the climate continues to change.

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19

Hinks, Anne, e Wendy Thomson. Genetics of juvenile rheumatic diseases. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199642489.003.0043_update_002.

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Juvenile rheumatic diseases are heterogeneous, complex genetic diseases; to date only juvenile idiopathic arthritis (JIA) has been extensively studied in terms of identifying genetic risk factors. The MHC region is a well-established risk factor but in the last few years candidate gene and large-scale genome-wide association studies have been utilized in the search for non-HLA risk factors. There are now 17 JIA susceptibility loci which reach the genome-wide significance threshold for association and a further 7 regions with evidence for association in more than one study. In addition, some subtype-specific associations are emerging. These risk loci now need to be investigated further using fine-mapping strategies and then appropriate functional studies to show how the variant alters the gene function. This knowledge will not only lead to a better understanding of disease pathogenesis for juvenile rheumatic diseases but may also aid in the classification of these heterogeneous diseases. It may identify new pathways for potential therapeutic targets and help in the prediction of disease outcome and response to treatment.

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20

Hinks, Anne, e Wendy Thomson. Genetics of juvenile rheumatic diseases. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199642489.003.0043_update_003.

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Juvenile rheumatic diseases are heterogeneous, complex genetic diseases; to date only juvenile idiopathic arthritis (JIA) has been extensively studied in terms of identifying genetic risk factors. The MHC region is a well-established risk factor but in the last few years candidate gene and large-scale genome-wide association studies have been utilized in the search for non-HLA risk factors. There are now 17 JIA susceptibility loci which reach the genome-wide significance threshold for association and a further 7 regions with evidence for association in more than one study. In addition, some subtype-specific associations are emerging. These risk loci now need to be investigated further using fine-mapping strategies and then appropriate functional studies to show how the variant alters the gene function. This knowledge will not only lead to a better understanding of disease pathogenesis for juvenile rheumatic diseases but may also aid in the classification of these heterogeneous diseases. It may identify new pathways for potential therapeutic targets and help in the prediction of disease outcome and response to treatment.

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21

Kotzer, Katrina E., e Sarah E. Kerr. Molecular Technologies and Test Issues. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190604929.003.0005.

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Molecular genetic testing has been around since the discovery and offering of clinical testing for the first gene sequenced. However, in recent years the methods and scope of molecular genetic testing have evolved significantly to encompass next-generation sequencing, multigene panels, and whole exome and genome testing. With this evolution in molecular methods, the nomenclature and variant evaluation and annotation processes are crucial for the systematic and standard interpretation of molecular test results. This chapter will provide the laboratory genetic counselor with information about the common sample types analyzed by molecular techniques for the purposes of genetic testing and the various methodologies available and their limitations. Guidelines are given for the standard approach to molecular variant reporting with respect to nomenclature and variant classification.

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22

Waterton, Claire. Barcoding Nature: Shifting Cultures of Taxonomy in an Age of Biodiversity Loss. Taylor & Francis Group, 2017.

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23

Ellis, Rebecca, Claire Waterton e Brian Wynne. Barcoding Nature: Shifting Cultures of Taxonomy in an Age of Biodiversity Loss. Taylor & Francis Group, 2016.

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24

Waterton, Claire. Barcoding Nature: Shifting Cultures of Taxonomy in an Age of Biodiversity Loss. Taylor & Francis Group, 2017.

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25

Hinks, Anne, e Wendy Thomson. Genetics of juvenile rheumatic diseases. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0043.

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Juvenile rheumatic diseases are heterogeneous, complex genetic diseases; to date only juvenile idiopathic arthritis (JIA) has been extensively studied in terms of identifying genetic risk factors. The MHC region is a well-established risk factor but in the last few years candidate gene and genome-wide association studies have been utilized in the search for non-HLA risk factors. There are now an additional 12 JIA susceptibility loci with evidence for association in more than one study. In addition, some subtype-specific associations are emerging. These risk loci now need to be investigated further using fine-mapping strategies and then appropriate functional studies to show how the variant alters the gene function. This knowledge will not only lead to a better understanding of disease pathogenesis for juvenile rheumatic diseases but may also aid in the classification of these heterogeneous diseases. It may identify new pathways for potential therapeutic targets and help in the prediction of disease outcome and response to treatment.

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26

Cerhan, James R., Claire M. Vajdic e John J. Spinelli. The Non-Hodgkin Lymphomas. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0040.

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The non-Hodgkin lymphomas (NHL) are a heterogeneous group of over forty lymphoid neoplasms that have undergone a major redefinition over the last twenty-five years, in part due to advances in immunology and genetics as well as implementation of the WHO classification system. NHLs are considered clonal tumors of B-cells, T-cells, or natural killer (NK) cells arrested at various stages of differentiation, regardless of whether they present in the blood (lymphoid leukemia) or lymphoid tissues (lymphoma). In the United States, the age-standardized NHL incidence rate (per 100,000) doubled from 1973 (10.2) to 2004 (21.4) and then stabilized, while five-year relative survival rates improved from 42% in 1973 to 70% in 2004. Established risk factors for NHL or specific NHL subtypes include infectious agents (HTLV-1, HIV, EBV, HHV8, HCV, H. pylori), immune dysregulation (primary immunodeficiency, transplantation, autoimmunity, and immunosuppressive drugs), family history of lymphoma, and common genetic variants identified by genome-wide association studies.

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27

Cooper, J. I. Viruses and the Environment. Springer, 2013.

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28

Cooper, J. I. Viruses and the Environment. Springer London, Limited, 2012.

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29

Barcoding Nature: Shifting Cultures of Taxonomy in an Age of Biodiversity Loss. Taylor & Francis Group, 2013.

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30

Barcoding Nature: Shifting Cultures of Taxonomy in an Age of Biodiversity Loss. Routledge, 2013.

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31

Ellis, Rebecca, Claire Waterton e Brian Wynne. Barcoding Nature: Shifting Cultures of Taxonomy in an Age of Biodiversity Loss. Taylor & Francis Group, 2013.

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32

Barcoding Nature. Routledge, 2014.

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33

Barcoding Nature. Taylor & Francis Group, 2013.

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34

Computational Methods in Systems Biology: 10th International Conference, CMSB 2012, London, UK, October 3-5, 2012, Proceedings. Springer, 2012.

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Livros sobre o tema "Genomic classification"

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