Advances in statistical bioinformatics: models and integrative inference for high-throughput data
Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in s...
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| Weitere Verfasser: | , , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2013
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xv, 481 pages) |
| ISBN: | 9781139226448 |
| DOI: | 10.1017/CBO9781139226448 |
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| dewey-ones | 572 - Biochemistry |
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| discipline | Biologie |
| doi_str_mv | 10.1017/CBO9781139226448 |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:18Z |
| institution | BVB |
| isbn | 9781139226448 |
| language | English |
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| spelling | Advances in statistical bioinformatics models and integrative inference for high-throughput data edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX. Cambridge Cambridge University Press 2013 1 online resource (xv, 481 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) Providing genome-informed personalized treatment is a goal of modern medicine. Identifying new translational targets in nucleic acid characterizations is an important step toward that goal. The information tsunami produced by such genome-scale investigations is stimulating parallel developments in statistical methodology and inference, analytical frameworks, and computational tools. Within the context of genomic medicine and with a strong focus on cancer research, this book describes the integration of high-throughput bioinformatics data from multiple platforms to inform our understanding of the functional consequences of genomic alterations. This includes rigorous and scalable methods for simultaneously handling diverse data types such as gene expression array, miRNA, copy number, methylation, and next-generation sequencing data. This material is written for statisticians who are interested in modeling and analyzing high-throughput data. Chapters by experts in the field offer a thorough introduction to the biological and technical principles behind multiplatform high-throughput experimentation Bioinformatics / Statistical methods Biometry Genetics / Technique Do, Kim-Anh 1960- edt Qin, Steven 1972- edt Vannucci, Marina 1966- edt Erscheint auch als Druckausgabe 978-1-107-02752-7 https://doi.org/10.1017/CBO9781139226448 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Advances in statistical bioinformatics models and integrative inference for high-throughput data Bioinformatics / Statistical methods Biometry Genetics / Technique |
| title | Advances in statistical bioinformatics models and integrative inference for high-throughput data |
| title_auth | Advances in statistical bioinformatics models and integrative inference for high-throughput data |
| title_exact_search | Advances in statistical bioinformatics models and integrative inference for high-throughput data |
| title_full | Advances in statistical bioinformatics models and integrative inference for high-throughput data edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX. |
| title_fullStr | Advances in statistical bioinformatics models and integrative inference for high-throughput data edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX. |
| title_full_unstemmed | Advances in statistical bioinformatics models and integrative inference for high-throughput data edited by Kim-Anh Do, The University of Texas M.D. Anderson Cancer Center, Zhaohui Steven Qin, Emory University, Atlanta, GA, Marina Vannucci, Rice University, Houston, TX. |
| title_short | Advances in statistical bioinformatics |
| title_sort | advances in statistical bioinformatics models and integrative inference for high throughput data |
| title_sub | models and integrative inference for high-throughput data |
| topic | Bioinformatics / Statistical methods Biometry Genetics / Technique |
| topic_facet | Bioinformatics / Statistical methods Biometry Genetics / Technique |
| url | https://doi.org/10.1017/CBO9781139226448 |
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