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

Hill, S. M ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1038/nmeth.3773
  3. Publisher: Nature Publishing Group , 2016
  4. Abstract:
  5. 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. © 2016 Nature America, Inc. All rights reserved
  6. Keywords:
  7. Phosphoprotein ; Protein tyrosine kinase ; Article ; Cancer cell line ; Causal modeling ; Causal molecular network ; Controlled study ; Human ; Human cell ; Learning ; Molecular biology ; Phenotype ; Priority journal ; Validity ; Algorithm ; Biological model ; Biology ; Gene regulatory network ; Genetics ; Neoplasm ; Procedures ; Protein analysis ; Tumor cell culture ; Algorithms ; Causality ; Computational Biology ; Computer Simulation ; Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; Models, Biological ; Neoplasms ; Protein Interaction Mapping ; Signal Transduction ; Software ; Systems Biology ; Tumor Cells, Cultured
  8. Source: Nature Methods ; Volume 13, Issue 4 , 2016 , Pages 310-322 ; 15487091 (ISSN)
  9. URL: http://www.nature.com/nmeth/journal/v13/n4/full/nmeth.3773.html