A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.

Concepts acquired on the effect of human protein privation have largely been based on information obtained from severely malnourished individuals, suffering multiple

Although originally based on a homogeneous perspective of language and, consequently, of the utterance and subjects – the discursive studies have developed

Based on the properties of optical burst flows and bandwidths provisioning in the networks, optimal solutions have been discussed related to OBS network architecture employing

disease genes. We found that the eCTFMining method could take into account of different network topological features of genes in the biological network to characterize their

Validation of the 28- joint Disease Activity Score (DAS28) and European League Against Rheumatism response criteria based on C-reactive protein against disease progression in

In the current study, the construction and computational analysis of protein interaction networks (PINs) based on expression data of proteins involved in 10 major cancer

To investigate the possibility of predicting recurrence of bladder tumors based on the clinical and morphological features of the disease and preoperative intravesical instillation

Most recently, Li and Patra [3] proposed a new method to prioritize disease genes by extending the random walk with restart algorithm on a heterogeneous network constructed