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}
  
}