Network Inference From Steady-State Data

Paola Lecca, Angela Re, Adaoha Ihekwaba, Ivan Mura, Thanh-Phuong Nguyen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Steady-state data are data that change little over time. In view of their serving the purposes of network inference, they are collected in experiments aiming specifically at gene network inference. We obtain steady-state data by knocking down, knocking out, or overexpressing genes. In this chapter, we present three approaches to infer gene networks from steady-state data. These algorithms belong to the three main classes of (1) correlation-based methods, (2) Z-score-based methods, and (3) regression-based methods. We have selected the simplest and most intuitive methods in each class (ie, the methods that pave the way to other more complex and sophisticated approaches in these three categories) to give to the reader the basic ideas for the procedures and their mathematical foundations.
Original languageEnglish
Title of host publicationComputational Systems Biology
Subtitle of host publicationInference and Modelling
Number of pages7
ISBN (Print)978-0-08-100095-3
Publication statusPublished - 2016

Publication series

NameComputational Systems Biology


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