Methods for Unweighted and Weighted Network Integration

Implementation of network integration approaches comprising unweighted and weighted integration methods. Unweighted integration is performed considering the average, per-edge average, maximum and minimum of networks edges. Weighted integration takes into account a weight for each network during the fusion process, where the weights express the ''predictiveness strength'' of each network considering a specific predictive task. Weights can be learned using a machine learning algorithm able to associate the weights to the assessment of the accuracy of the learning algorithm trained on the network itself. The implemented methods can be applied to effectively integrate different biological networks modelling a wide range of problems in bioinformatics (e.g. disease gene prioritization, protein function prediction, drug repurposing, clinical outcome prediction).

NetInt

This repository contains different network integration methods that can be classified into:

Unweighted approaches These methods perform network integration without considering the "predictiveness" (i.e. the informativeness of each network) with respect to a prediction task. In particular, the following integrations are implemented:

• Unweighted Average (UA)
• Per-edge Unweighted Average (PUA)
• Maximum (MAX)
• Minimum (MIN)
• At least K (ATLEASTK)

Weighted approaches These methods require to provide as input a weight for each network, which are usually learned considering an appropriate learning algorithm:

• Weighted Average Per-class (WAP)
• Weighted Average (WA)

Citation - These methods were presented in the paper:

Valentini, Giorgio, et al. "An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods." Artificial Intelligence in Medicine 61.2 (2014): 63-78.

Corresponding bib entry:

@article{valentini2014extensive,
  title={An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods},
  author={Valentini, Giorgio and Paccanaro, Alberto and Caniza, Horacio and Romero, Alfonso E and Re, Matteo},
  journal={Artificial Intelligence in Medicine},
  volume={61},
  number={2},
  pages={63--78},
  year={2014},
  publisher={Elsevier}
  
}