Inferring gene regulatory networks is a difficult problem to solve due
to the relative scarcity of data compared to the potential size of the
networks. While researchers have developed techniques to find some of
the underlying network structure, there is still no one-size-fits-all
algorithm for every data set.
Network Inference in Molecular Biology examines the current techniques
used by researchers, and provides key insights into which algorithms
best fit a collection of data. Through a series of in-depth examples,
the book also outlines how to mix-and-match algorithms, in order to
create one tailored to a specific data situation.
Network Inference in Molecular Biology is intended for advanced-level
students and researchers as a reference guide. Practitioners and
professionals working in a related field will also find this book
valuable.