| TÃtulo : |
Extended Abstracts Fall 2015 : Biomedical Big Data; Statistics for Low Dose Radiation Research |
| Tipo de documento: |
documento electrónico |
| Autores: |
Ainsbury, Elizabeth A., ; Calle, M.Luz, ; Cardis, Elisabeth, ; Einbeck, Jochen, ; Gómez, Guadalupe, ; Puig, Pere, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2017 |
| Número de páginas: |
VII, 131 p. 24 ilustraciones, 17 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-319-55639-0 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Idioma : |
Inglés (eng) |
| Palabras clave: |
BiometrÃa Biomatemáticas BioestadÃstica BiologÃa Matemática y Computacional |
| Clasificación: |
570.15195 |
| Resumen: |
Este volumen de dos partes reúne resúmenes ampliados de conferencias correspondientes a charlas seleccionadas del "Taller de Biostatnet sobre (grandes) datos biomédicos" y del "DoReMi LD-RadStats: Taller para estadÃsticos interesados ​​en contribuir a la investigación sobre radiación de dosis bajas en la UE", que fueron celebradas en el Centre de Recerca Matemà tica (CRM) de Barcelona del 26 al 27 de noviembre de 2015, y en el Institut de Salut Global ISGlobal (antiguo CREAL) del 26 al 28 de octubre de 2015, respectivamente. La mayorÃa de las contribuciones son artÃculos breves que presentan nuevos resultados preliminares que aún no se han publicado en revistas de investigación periódicas. La primera parte está dedicada a los retos que supone analizar el llamado "Biomedical Big Data", enormes cantidades de datos biomédicos y sanitarios que se generan cada dÃa gracias al uso de los recientes avances tecnológicos como la secuenciación genómica masiva, la historia clÃnica electrónica o la alta tecnologÃa. resolución de imágenes médicas, entre otros. El análisis de esta información plantea importantes desafÃos para los investigadores en los campos de la bioestadÃstica, la bioinformática y el procesamiento de señales. Además, también se discuten otros desafÃos relevantes en la investigación bioestadÃstica, que no necesariamente involucran big data. A su vez, la segunda parte está dedicada a la investigación de radiaciones de bajas dosis, donde es necesario comprender y caracterizar completamente las posibles fuentes de incertidumbre antes de poder reducirlas. Además, el libro demuestra por qué el análisis de incertidumbre formal tiene el potencial de proporcionar una plataforma común para la investigación multidisciplinaria en este campo. Este libro está dirigido a investigadores establecidos, asà como a estudiantes de doctorado y postdoctorado que quieran aprender más sobre los últimos avances en estas áreas de investigación tan activas. |
| Nota de contenido: |
PART I -- Foreword -- Extreme Observations in Biomedical Data -- An Ordinal Joint Model for Breast Cancer -- Sample Size Impact on the Categorisation of Continuous Variables in Clinical Prediction -- ntegrative Analysis of Transcriptomics and Proteomics Data for the Characterization of Brain Tissue After Ischemic Stroke -- Applying INAR-Hidden Markov Chains in the Analysis of Under-Reported Data -- Joint Modelling for Flexible Multivariate Longitudinal and Survival Data: Application in Orthotopic Liver Transplantation -- A Multi-State Model for the Progression to Osteopenia and Osteoporosis among HIV-Infected Patients -- Statistical Challenges for Human Microbiome Analysis -- Integrative Analysis to Select Genes Regulated by Methylation in a Cancer Colon Study -- Topological Pathway Enrichment Analysis of Gene Expression in High Grade Serous Ovarian Cancer Reveals Tumor-Stoma Cross-Talk.-PART II -- Foreword -- Biological Dosimetry, Statistical Challenges: Biological Dosimetry after High-Dose Exposures to Ionizing Radiation -- Heterogeneous Correlation of Multi-Level Omics Data for the Consideration of Inter-Tumoural Heterogeneity -- Overview of Topics Related to Model Selection for Regression -- Understanding Plaque Overlap is Essential for Modelling Radiation Induced Atherosclerosis -- On the Use of Random Effect Models for Radiation Biodosimetry -- Modelling of the Radiation Carcinogenesis: the Analytic and Stochastic Approaches -- Bayesian Solutions to Biodosimetry Count Data Problems and Supporting Software -- Empirical Assessment of Gene Expression Biomarkers for Radiation Exposure -- Poisson-Weighted Estimation by Discrete Kernel with Application to Radiation Biodosimetry -- R Implementation of the Excess Relative Rate Model: Applications to Radiation Epidemiology -- Uncertainty Considerations Following a Mechanistic Analysis of Lung Cancer Mortality. |
| Tipo de medio : |
Computadora |
| Summary : |
This two-part volume gathers extended conference abstracts corresponding to selected talks from the "Biostatnet workshop on Biomedical (Big) Data" and from the "DoReMi LD-RadStats: Workshop for statisticians interested in contributing to EU low dose radiation research", which were held at the Centre de Recerca Matemàtica (CRM) in Barcelona from November 26th to 27th, 2015, and at the Institut de Salut Global ISGlobal (former CREAL) from October 26th to 28th, 2015, respectively. Most of the contributions are brief articles, presenting preliminary new results not yet published in regular research journals. The first part is devoted to the challenges of analyzing so called "Biomedical Big Data", tremendous amounts of biomedical and health data that are generated every day due to the use of recent technological advances such as massive genomic sequencing, electronic health records or high-resolution medical imaging, among others. The analysis of this information poses significant challenges for researchers in the fields of biostatistics, bioinformatics, and signal processing. Furthermore, other relevant challenges in biostatistical research, not necessarily involving big data, are also discussed. In turn, the second part is dedicated to low dose radiation research, where there is a need to fully understand and characterize potential sources of uncertainty before they can be reduced. Further, the book demonstrates why formal uncertainty analysis has the potential to provide a common platform for multidisciplinary research in this field. This book is intended for established researchers, as well as for PhD and postdoctoral students who want to learn more about the latest advances in these highly active areas of research. |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
Extended Abstracts Fall 2015 : Biomedical Big Data; Statistics for Low Dose Radiation Research [documento electrónico] / Ainsbury, Elizabeth A., ; Calle, M.Luz, ; Cardis, Elisabeth, ; Einbeck, Jochen, ; Gómez, Guadalupe, ; Puig, Pere, . - 1 ed. . - [s.l.] : Springer, 2017 . - VII, 131 p. 24 ilustraciones, 17 ilustraciones en color. ISBN : 978-3-319-55639-0 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés ( eng)
| Palabras clave: |
BiometrÃa Biomatemáticas BioestadÃstica BiologÃa Matemática y Computacional |
| Clasificación: |
570.15195 |
| Resumen: |
Este volumen de dos partes reúne resúmenes ampliados de conferencias correspondientes a charlas seleccionadas del "Taller de Biostatnet sobre (grandes) datos biomédicos" y del "DoReMi LD-RadStats: Taller para estadÃsticos interesados ​​en contribuir a la investigación sobre radiación de dosis bajas en la UE", que fueron celebradas en el Centre de Recerca Matemà tica (CRM) de Barcelona del 26 al 27 de noviembre de 2015, y en el Institut de Salut Global ISGlobal (antiguo CREAL) del 26 al 28 de octubre de 2015, respectivamente. La mayorÃa de las contribuciones son artÃculos breves que presentan nuevos resultados preliminares que aún no se han publicado en revistas de investigación periódicas. La primera parte está dedicada a los retos que supone analizar el llamado "Biomedical Big Data", enormes cantidades de datos biomédicos y sanitarios que se generan cada dÃa gracias al uso de los recientes avances tecnológicos como la secuenciación genómica masiva, la historia clÃnica electrónica o la alta tecnologÃa. resolución de imágenes médicas, entre otros. El análisis de esta información plantea importantes desafÃos para los investigadores en los campos de la bioestadÃstica, la bioinformática y el procesamiento de señales. Además, también se discuten otros desafÃos relevantes en la investigación bioestadÃstica, que no necesariamente involucran big data. A su vez, la segunda parte está dedicada a la investigación de radiaciones de bajas dosis, donde es necesario comprender y caracterizar completamente las posibles fuentes de incertidumbre antes de poder reducirlas. Además, el libro demuestra por qué el análisis de incertidumbre formal tiene el potencial de proporcionar una plataforma común para la investigación multidisciplinaria en este campo. Este libro está dirigido a investigadores establecidos, asà como a estudiantes de doctorado y postdoctorado que quieran aprender más sobre los últimos avances en estas áreas de investigación tan activas. |
| Nota de contenido: |
PART I -- Foreword -- Extreme Observations in Biomedical Data -- An Ordinal Joint Model for Breast Cancer -- Sample Size Impact on the Categorisation of Continuous Variables in Clinical Prediction -- ntegrative Analysis of Transcriptomics and Proteomics Data for the Characterization of Brain Tissue After Ischemic Stroke -- Applying INAR-Hidden Markov Chains in the Analysis of Under-Reported Data -- Joint Modelling for Flexible Multivariate Longitudinal and Survival Data: Application in Orthotopic Liver Transplantation -- A Multi-State Model for the Progression to Osteopenia and Osteoporosis among HIV-Infected Patients -- Statistical Challenges for Human Microbiome Analysis -- Integrative Analysis to Select Genes Regulated by Methylation in a Cancer Colon Study -- Topological Pathway Enrichment Analysis of Gene Expression in High Grade Serous Ovarian Cancer Reveals Tumor-Stoma Cross-Talk.-PART II -- Foreword -- Biological Dosimetry, Statistical Challenges: Biological Dosimetry after High-Dose Exposures to Ionizing Radiation -- Heterogeneous Correlation of Multi-Level Omics Data for the Consideration of Inter-Tumoural Heterogeneity -- Overview of Topics Related to Model Selection for Regression -- Understanding Plaque Overlap is Essential for Modelling Radiation Induced Atherosclerosis -- On the Use of Random Effect Models for Radiation Biodosimetry -- Modelling of the Radiation Carcinogenesis: the Analytic and Stochastic Approaches -- Bayesian Solutions to Biodosimetry Count Data Problems and Supporting Software -- Empirical Assessment of Gene Expression Biomarkers for Radiation Exposure -- Poisson-Weighted Estimation by Discrete Kernel with Application to Radiation Biodosimetry -- R Implementation of the Excess Relative Rate Model: Applications to Radiation Epidemiology -- Uncertainty Considerations Following a Mechanistic Analysis of Lung Cancer Mortality. |
| Tipo de medio : |
Computadora |
| Summary : |
This two-part volume gathers extended conference abstracts corresponding to selected talks from the "Biostatnet workshop on Biomedical (Big) Data" and from the "DoReMi LD-RadStats: Workshop for statisticians interested in contributing to EU low dose radiation research", which were held at the Centre de Recerca Matemàtica (CRM) in Barcelona from November 26th to 27th, 2015, and at the Institut de Salut Global ISGlobal (former CREAL) from October 26th to 28th, 2015, respectively. Most of the contributions are brief articles, presenting preliminary new results not yet published in regular research journals. The first part is devoted to the challenges of analyzing so called "Biomedical Big Data", tremendous amounts of biomedical and health data that are generated every day due to the use of recent technological advances such as massive genomic sequencing, electronic health records or high-resolution medical imaging, among others. The analysis of this information poses significant challenges for researchers in the fields of biostatistics, bioinformatics, and signal processing. Furthermore, other relevant challenges in biostatistical research, not necessarily involving big data, are also discussed. In turn, the second part is dedicated to low dose radiation research, where there is a need to fully understand and characterize potential sources of uncertainty before they can be reduced. Further, the book demonstrates why formal uncertainty analysis has the potential to provide a common platform for multidisciplinary research in this field. This book is intended for established researchers, as well as for PhD and postdoctoral students who want to learn more about the latest advances in these highly active areas of research. |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
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