Inferring cellular networks – a review

By Florian Markowetz, Rainer Spang. 2007

438 citations recorded by the source corpus.

In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

Automated adaptive inference of phenomenological dynamical models (2015, neutral): “This is trivially useful in the context of engineering and control, where predictive, usable models are often necessarily far removed from microscopic precision [21].”
A human functional protein interaction network and its application to cancer data analysis (2010, positive): “Many computational methods have been developed to generate biological pathways and networks based on DNA microarray or other similar data sets from scratch with promising results [81].”
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, background): “These advances have prompted the development of a plethora of models of GRNs and algorithms to reverse-engineer them from expression data [2–5].”
Network Inference and Biological Dynamics (2011, background): “re as well as in bioinformatics and bioengineering, with some popular approaches reviewed in Bansal, Belcastro and Ambesi-Impiombato (2007); Bonneau (2008); Hecker et al. (2009); Lee and Tzou (2009); Markowetz and Spang (2007). Graphical models play a prominent role in this literature, as does varia”
Unified framework for modeling multivariate distributions in biological sequences (2019, methodology): “For the mean-ﬁeld approximation of MRFs (Morcos et al., 2011) or MG models (Baldassi et al., 2014), the precision (or inverse covariance) matrix is used to infer ”direct causa-tion” (Markowetz & Spang, 2007).”
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection (2012, methodology): “Moreover, running TIGRESS online is possible on GenePattern: http://www.broadinstitute.org/ cancer/software/genepattern/.”
Robust informational entropy-based descriptors of flow in catchment hydrology (2016, background): “While time series partial correlation analysis can extract properties of coupling between different variables, it is unable to capture the strength of nonlinear relationships between different variables (Knuth 2005, Markowetz and Spang 2007).”
Inferring causal molecular networks: empirical assessment through a community-based effort (2016, background): “05 and (2) m m s i c i c i c , , , D I − > , where σi,c is the pooled replicate s.”