TY - CHAP
T1 - Probabilistic Computational Causal Discovery for Systems Biology
AU - Lagani, Vincenzo
AU - Triantafillou, Sofia
AU - Ball, Gordon
AU - Tegnér, Jesper
AU - Tsamardinos, Ioannis
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-23
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
UR - https://link.springer.com/10.1007/978-3-319-21296-8_3
UR - http://www.scopus.com/inward/record.url?scp=85085205738&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-21296-8_3
DO - 10.1007/978-3-319-21296-8_3
M3 - Chapter
SP - 33
EP - 73
BT - Studies in Mechanobiology, Tissue Engineering and Biomaterials
PB - Springer
ER -