An introduction to a new paradigm in social, technological, and scientific discourse, this book presents an overview of statistical methods for describing, modeling, and inferring biological networks using genomic and other types of data. It covers a large variety of modern statistical techniques, such as sparse graphical models, state space models, Boolean networks, and hidden Markov models. The authors address gene transcription data, microRNAs, ChIP-chip, and RNAi data. Along with end-of-chapter exercises, the text includes many real-world examples with implementations using a dedicated R package.
An introduction to a new paradigm in social, technological, and scientific discourse, this book presents an overview of statistical methods for describing, modeling, and inferring biological networks using genomic and other types of data. It covers a large variety of modern statistical techniques, such as sparse graphical models, state space models, Boolean networks, and hidden Markov models. The authors address gene transcription data, microRNAs, ChIP-chip, and RNAi data. Along with end-of-chapter exercises, the text includes many real-world examples with implementations using a dedicated R package.
Statistics for Biological Networks: How to Infer Networks from Data / Edition 1
320
Statistics for Biological Networks: How to Infer Networks from Data / Edition 1
320Hardcover
Product Details
| ISBN-13: | 9781439841471 |
|---|---|
| Publisher: | Taylor & Francis |
| Publication date: | 06/28/2026 |
| Series: | Chapman & Hall/CRC Interdisciplinary Statistics |
| Pages: | 320 |
| Product dimensions: | 6.12(w) x 9.19(h) x (d) |