Calculates confidence scores for user-specified sets of interactions. IntScore provides six network topology- and annotation-based confidence scoring methods. It also enables the integration of scores calculated by the different methods into an aggregate score using machine learning approaches. IntScore can serve experimentalists to increase the quality of data produced by interaction screens and assess the performance of those screens, and can help computational biologists to increase the reliability of network-based inferences by controlling the accuracy of the input interaction data.
Vertebrate Genomics Department, Max Planck Institute for Molecular Genetics, Berlin, Germany
IntScore funding source(s)
The European Commission under its Seventh Framework Programme with the grant diXa ; German Ministry of Education and Research [MedSys PREDICT, 0315428A; NGFNp, NeuroNet-TP3, 01GS08171]; Max Planck Society