| Description |
1 online resource (276 p.) |
| Contents |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1. Knowledge Integration -- Chapter 1. Clinical Data Warehouses -- 1.1. Introduction to clinical information systems and biomedical warehousing: data warehouses for what purposes? -- 1.1.1. Warehouse history -- 1.1.2. Using data warehouses today -- 1.2. Challenge: widely scattered data -- 1.3. Data warehouses and clinical data -- 1.3.1. Warehouse structures -- 1.3.2. Warehouse construction and supply -- 1.3.3. Uses -- 1.4. Warehouses and omics data: challenges -- 1.4.1. Challenges of data volumetry and structuring omic data |
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1.4.2. Attempted solutions -- 1.5. Challenges and prospects -- 1.5.1. Toward general-purpose warehouses -- 1.5.2. Ethical dimension of the implementation and the use of warehouses -- 1.5.3. Origin and reproducibility -- 1.5.4. Data quality -- 1.5.5. Data warehousing federation and data sharing -- 1.6. References -- Chapter 2. Semantic Web Methods for Data Integration in Life Sciences -- 2.1. Data-related requirements in life sciences -- 2.1.1. Databases for the life sciences -- 2.1.2. Requirements -- 2.1.3. Common approaches: InterMine and BioMart -- 2.2. Semantic Web -- 2.2.1. Techniques |
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2.2.2. Implementation -- 2.3. Perspectives -- 2.3.1. Facilitating appropriation to users -- 2.3.2. Facilitating the appropriation by software programs: FAIR data -- 2.3.3. Federated queries -- 2.4. Conclusion -- 2.5. References -- Chapter 3. Workflows for Bioinformatics Data Integration -- 3.1. Introduction -- 3.2. Bioinformatics data processing chains: difficulties -- 3.2.1. Designing a data processing chain -- 3.2.2. Analysis execution and reproducibility -- 3.2.3. Maintenance, sharing and reuse -- 3.3. Solutions provided by scientific workflow systems -- 3.3.1. Fundamentals of workflow systems |
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3.3.2. Workflow systems -- 3.4. Use case: RNA-seq data analysis -- 3.4.1. Study description -- 3.4.2. From data processing chain to workflows -- 3.4.3. Data processing chains implemented as workflows: conclusion -- 3.5. Challenges, open problems and research opportunities -- 3.5.1. Formalizing workflow development -- 3.5.2. Workflow testing -- 3.5.3. Discovering and sharing workflows -- 3.6. Conclusion -- 3.7. References -- Part 2. Integration and Statistics -- Chapter 4. Variable Selection in the General Linear Model: Application to Multiomic Approaches for the Study of Seed Quality |
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4.1. Introduction -- 4.2. Methodology -- 4.2.1. Estimation of the covariance matrix ƒ°q -- 4.2.2. Estimation of B -- 4.3. Numerical experiments -- 4.3.1. Statistical performance -- 4.3.2. Numerical performance -- 4.4. Application to the study of seed quality -- 4.4.1. Metabolomics data -- 4.4.2. Proteomics data -- 4.5. Conclusion -- 4.6. Appendices -- 4.6.1. Example of using the package MultiVarSel for metabolomic data analysis -- 4.6.2. Example of using the package MultiVarSel for proteomic data analysis -- 4.7. Acknowledgments -- 4.8. References |
| Notes |
Description based upon print version of record |
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Chapter 5. Structured Compression of Genetic Information and Genome-Wide Association Study by Additive Models |
| Form |
Electronic book
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| Author |
Martin-Magniette, Marie-Laure
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Rigaill, Guillem
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| ISBN |
9781394257294 |
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1394257295 |
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