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Semi-supervised Multi-View Learning for Gene Network Reconstruction

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posted on 17.11.2015 by Gianvito Pio

Semi-supervised Multi-View Learning for Gene Network Reconstruction

 

SynTReN Data:

E.coli and Yeast sub-networks, generated expression data and gold standards (Input_Datasets.zip)

Interactions predicted by base methods (Base_Method_Predictions.zip)

Interactions predicted by our approach - Clustering performed with PCA (Predictions.zip)

Interactions predicted by our approach - Clustering performed with K-means (PredictionsK.zip)

 

Dream5 Data:

Expression data and gold standards provided by Marbach et al. 2012 [1] (Input_Datasets_D5.zip)

Interactions predicted by the considered DREAM5 base methods provided by Marbach et al. 2012 [1] (Base_Method_Predictions_D5.zip)

Interactions predicted by our approach - Clustering performed with PCA (Predictions_D5.zip)

Interactions predicted by our approach - Clustering performed with K-means (PredictionsK_D5.zip)

 

 

[1] Marbach, D., Costello, J. C., Kuffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., and Stolovitzky, G., Wisdom of crowds for robust gene network inference, Nature Methods, 9, 796-804, 2012.

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