This book introduces a number of cutting edge statistical methods which can be used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze asΒ much data as possible in a given biological system (such as a cell
Applied Statistics for Network Biology: Methods in Systems Biology (Quantitative and Network Biology (VCH))
β Scribed by Matthias Dehmer, Frank Emmert-Streib, Armin Graber, Armindo Salvador
- Publisher
- Wiley-VCH
- Year
- 2011
- Tongue
- English
- Leaves
- 462
- Series
- Quantitative and Network Biology (VCH)
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
This book introduces a number of cutting edge statistical methods which can be used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze asΒ much data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed at experimental researcher as well as computational biologists who often lack an appropriate background in statistical analysis.
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This book introduces a number of cutting edge statistical methods which can be used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze asΒ much data as possible in a given biological system (such as a cell
The book introduces to the reader a number of cutting edge statistical methods which can e used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as many data as possible in a given biological system (su
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