Sub-Challenge 4: Species Specific Network Inference

Sub-Challenge 4: Species Specific Network Inference

Sub-Challenge 4 is closed

 

Description 

The goal of sub-challenge 4 is to infer human and rat networks given phosphoprotein, gene expression and cytokine data and a reference network provided as prior knowledge. Participants will use network inference to add or remove edges from the reference map in order to produce specific rat and human networks (Fig. SC4.A).

Figure SC4.A: In sub-challenge 4, a Reference Network is provided to participants. Participants are asked to construct human- and rat-specific networks given the omics data that is provided. Participants will use network inference to add or remove edges from the Reference Network based on Phosphoprotein (P), Gene Expression (GEx) and Cytokine (Cy) data in training sets for rat and human. Only human and rat data from subset A must be used to allow for proper comparability between the respective networks.

Background  

Systems biology emphasizes the study of relationships and connectivity between the components of a complex system. As such, pathway diagrams are the primary representation of complex biological systems, and the construction of accurate and complete pathway maps is an on-going challenge in the field. The two main approaches that have been taken to build pathway maps are knowledge driven and data driven. The knowledge-driven approach uses a priori data, often curated from the literature, to define entities (nodes) and connections (edges) that can be assembled into network diagrams. In contrast, the data-driven approach seeks to infer the connection based on inference from large dataset using methods such as regression analysis and Bayesian probabilistic models. Combining disparate data types in pathway maps is a useful way of synthesizing such diverse knowledge into a consistent and unified view of a complex biological system. In addition, knowledge-driven approaches are often used to construct the scaffold network that can be augmented and refined using data-driven approaches.

 

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