1. Learning and inference in computational systems biology.
- Author
-
Girolami, Mark, Rattray, Magnus, Sanguinetti, Guido, and Lawrence, Neil
- Subjects
Bioinformatics -- Statistical methods ,Inference ,Machine learning - Abstract
Summary: Computational systems biology unifies the mechanistic approach of systems biology with the data-driven approach of computational biology. Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model-in other words, to answer specific questions about the underlying mechanisms of a biological system-in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks.The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered.
- Published
- 2010