Inferring causal molecular networks: empirical assessment through a community-based effort

By S. Hill, Laura M. Heiser, T. Cokelaer, Michael Unger, Nicole K. Nesser, Daniel E. Carlin, Yang Zhang, A. Sokolov, Evan O. Paull, Christopher K. Wong and 28 more. 2016

222 citations recorded by the source corpus.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Learning Neural Causal Models from Unknown Interventions (2019, background): “Causal structure learning using SCMs has been attempted in several disciplines including biology [2, 3], weather forecasting [4] and medicine [5, 6].”
Learning Large Scale Ordinary Differential Equation Systems (2017, methodology): “The winning team constructed edge weights via tests for nonlinear functional relations without the constraints of an ODE system, and were in this way better able to capture the correct network structure (see Supplementary material on Team 7 in Hill et al. (2016)).”
Robust generalised Bayesian inference for intractable likelihoods (2021, unknown): “However, it is known that existing approaches to network inference are non-robust, in a general sense, with community challenges regularly highlighting the different conclusions drawn by different estimators applied to an identical dataset (Hill et al., 2016).”
Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases (2018, background): “DREAM challenges have established standardized resources and robust methodologies for diverse problems, including the inference of gene regulatory and signaling networks (Hill et al., 2016; Marbach et al., 2012).”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, methodology): “…networks to form a community or ensemble prediction often confers slight but consistent improvements in the quality of the predicted network (Hill et al. 2016, Marbach et al. 2010, Marbach et al. 2012), but how to best combine and weight different methods to form a consensus prediction is…”
Repair of Partly Misspecified Causal Diagrams. (2017, background): “17,18 Our proposal is to balance automated procedures with expert knowledge: reverting to expert-driven analysis when there is insufficient data to contradict expert-supplied edges.”