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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}
}These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.