Advances in Statistical Bioinformatics : Models and Integrative Inference for High-Throughput Data
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| Title: | Advances in Statistical Bioinformatics : Models and Integrative Inference for High-Throughput Data |
|---|---|
| Description: | 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. |
| Authors: | Kim-Anh Do, Zhaohui Steve Qin, Marina Vannucci |
| Resource Type: | eBook. |
| Subjects: | Genetics--Technique, Bioinformatics--Statistical methods, Biometry |
| Categories: | COMPUTERS / Data Science / Bioinformatics |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advances in Statistical Bioinformatics : Models and Integrative Inference for High-Throughput Data – Name: Abstract Label: Description Group: Ab 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 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kim-Anh+Do%22">Kim-Anh Do</searchLink><br /><searchLink fieldCode="AR" term="%22Zhaohui+Steve+Qin%22">Zhaohui Steve Qin</searchLink><br /><searchLink fieldCode="AR" term="%22Marina+Vannucci%22">Marina Vannucci</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Genetics--Technique%22">Genetics--Technique</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics--Statistical+methods%22">Bioinformatics--Statistical methods</searchLink><br /><searchLink fieldCode="DE" term="%22Biometry%22">Biometry</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Bioinformatics%22">COMPUTERS / Data Science / Bioinformatics</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 572.80285 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Genetics--Technique Type: general – SubjectFull: Bioinformatics--Statistical methods Type: general – SubjectFull: Biometry Type: general Titles: – TitleFull: Advances in Statistical Bioinformatics : Models and Integrative Inference for High-Throughput Data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kim-Anh Do – PersonEntity: Name: NameFull: Zhaohui Steve Qin – PersonEntity: Name: NameFull: Marina Vannucci – PersonEntity: Name: NameFull: Kim-Anh Do – PersonEntity: Name: NameFull: Zhaohui Steve Qin – PersonEntity: Name: NameFull: Marina Vannucci IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2013 – D: 04 M: 02 Type: profile Y: 2014 Identifiers: – Type: isbn-print Value: 9781107027527 – Type: isbn-electronic Value: 9781107250246 Titles: – TitleFull: Advances in Statistical Bioinformatics : Models and Integrative Inference for High-Throughput Data Type: main |
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