Difference between revisions of "BIN-Data integration"
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Revision as of 13:33, 30 September 2017
Data Integration
Keywords: Integration of biological data; Identifier mapping; Source and result of integration; Domain of validity of annotations (time, tissue, position...); Data and metadata.
Contents
This unit is under development. There is some contents here but it is incomplete and/or may change significantly: links may lead to nowhere, the contents is likely going to be rearranged, and objectives, deliverables etc. may be incomplete or missing. Do not work with this material until it is updated to "live" status.
Abstract
...
This unit ...
Prerequisites
You need to complete the following units before beginning this one:
- BIN-EBI (Databases and services at the EBI)
- BIN-FUNC-Databases (Molecular Function Databases)
- BIN-Miscellaneous_DB (Miscellaneous Databases for Bioinformatics)
- BIN-NCBI (The NCBI Database and Services)
- BIN-PDB (The RCSB-PDB Structure Database)
Objectives
...
Outcomes
...
Deliverables
- Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
- Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
- Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.
Evaluation
Evaluation: NA
- This unit is not evaluated for course marks.
Contents
Task:
- Read the introductory notes on concepts and approaches to data integration in bioinformatics.
Further reading, links and resources
| Xie & Ahn (2010) Statistical methods for integrating multiple types of high-throughput data. Methods Mol Biol 620:511-29. (pmid: 20652519) |
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[ PubMed ] [ DOI ] Large-scale sequencing, copy number, mRNA, and protein data have given great promise to the biomedical research, while posing great challenges to data management and data analysis. Integrating different types of high-throughput data from diverse sources can increase the statistical power of data analysis and provide deeper biological understanding. This chapter uses two biomedical research examples to illustrate why there is an urgent need to develop reliable and robust methods for integrating the heterogeneous data. We then introduce and review some recently developed statistical methods for integrative analysis for both statistical inference and classification purposes. Finally, we present some useful public access databases and program code to facilitate the integrative analysis in practice. |
Notes
Self-evaluation
If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.
About ...
Author:
- Boris Steipe <boris.steipe@utoronto.ca>
Created:
- 2017-08-05
Modified:
- 2017-08-05
Version:
- 0.1
Version history:
- 0.1 First stub
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