Treeworks is our new algorithm for manageable network construction and illuminating crosstalk between known signaling pathways. 

The problem:  often, within high throughput experimental data, known signaling pathways and protein-protein integration maps are incomplete. Not all of the components of each signaling cascade are identified, and thus, researchers miss both direct and indirect interactions.   These missing links are often critical for the interpretation of functional significance within the data.   

 Hey, it's a hair-ball...

With Treeworks, we can connect seemingly unconnected data to find relevant interactions.  For example; connecting phosphoproteomic with transcriptomic data without the seemingly linking proteomics data.  

 TreeWorks solution to 425 differentially expressed phosphoproteins in patient AML blasts compared with healthy control

TreeWorks solution to 425 differentially expressed phosphoproteins in patient AML blasts compared with healthy control

 

Treeworks offers the connection between these indirect and allegedly unconnected datasets by;

  • Calculating significance and confidence of expression in each experimental dataset. 
  • Defining and placing a confidence factor on every node (terminal or not) in a newly generated hidden graph.
  • Identifying edges and associated confidence weights of the hidden graph.
  • Balance the costs of including each individual node with the benefit of excluding each edge to construct the graph network. 
  • Avoid producing large ‘hair-ball’ networks that include irrelevant nodes and numerous edges.
  • Discover coherent signaling pathways that efficiently and powerfully describe interconnected biological processes despite diverse experimental data. 

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