Probabilistic Computational Causal Discovery for Systems Biology

Vincenzo Lagani, Sofia Triantafillou, Gordon Ball, Jesper Tegnér, Ioannis Tsamardinos

Research output: Chapter in Book/Report/Conference proceedingChapter

23 Scopus citations


Discovering the causal mechanisms of biological systems is necessary to design new drugs and therapies. Computational Causal Discovery (CD) is a field that offers the potential to discover causal relations and causal models under certain conditions with a limited set of interventions/manipulations. This chapter reviews the basic concepts and principles of CD, the nature of the assumptions to enable it, potential pitfalls in its application, and recent advances and directions. Importantly, several success stories in molecular and systems biology are discussed in detail.
Original languageEnglish (US)
Title of host publicationStudies in Mechanobiology, Tissue Engineering and Biomaterials
Number of pages41
StatePublished - Jan 1 2016
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23


Dive into the research topics of 'Probabilistic Computational Causal Discovery for Systems Biology'. Together they form a unique fingerprint.

Cite this