| Title: | Refining Cellular Pathway Models Using an Ensemble of Heterogeneous Data Sources |
| Author(s): | Franks AM; Markowetz F; Airoldi EM; |
| Address: | "Department of Statistics and, Applied Probability, University of California, Santa Barbara, South Hall, Santa Barbara, California 93106, USA. Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, United Kingdom. Fox School of Business, Department of Statistical Science, Temple University, Center for Data Science, 1810 Liacouras Walk, Philadelphia, Pennsylvania 19122, USA" |
| ISSN/ISBN: | 1932-6157 (Print) 1941-7330 (Electronic) 1932-6157 (Linking) |
| Abstract: | "Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae" |
| Keywords: | Bayesian inference Multi-level modeling regulation and signaling dynamics statistical network analysis; |
| Notes: | "PubMed-not-MEDLINEFranks, Alexander M Markowetz, Florian Airoldi, Edoardo M eng R01 GM096193/GM/NIGMS NIH HHS/ 2018/09/01 Ann Appl Stat. 2018 Sep; 12(3):1361-1384. doi: 10.1214/16-aoas915. Epub 2018 Sep 11" |
