Protein-protein interaction prediction based on multiple kernels and partial network with linear programming
Date
2016-08-01
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BioMed Central
Abstract
BACKGROUND: Prediction of de novo protein-protein interaction is a critical step toward reconstructing PPI networks,
which is a central task in systems biology. Recent computational approaches have shifted from making PPI prediction
based on individual pairs and single data source to leveraging complementary information from multiple
heterogeneous data sources and partial network structure. However, how to quickly learn weights for heterogeneous
data sources remains a challenge. In this work, we developed a method to infer de novo PPIs by combining multiple
data sources represented in kernel format and obtaining optimal weights based on random walk over the existing
partial networks.
RESULTS: Our proposed method utilizes Barker algorithm and the training data to construct a transition matrix which
constrains how a random walk would traverse the partial network. Multiple heterogeneous features for the proteins in
the network are then combined into the form of weighted kernel fusion, which provides a new "adjacency matrix" for
the whole network that may consist of disconnected components but is required to comply with the transition matrix
on the training subnetwork. This requirement is met by adjusting the weights to minimize the element-wise
difference between the transition matrix and the weighted kernels. The minimization problem is solved by linear
programming. The weighted kernel fusion is then transformed to regularized Laplacian (RL) kernel to infer missing or
new edges in the PPI network, which can potentially connect the previously disconnected components.
CONCLUSIONS: The results on synthetic data demonstrated the soundness and robustness of the proposed algorithms
under various conditions. And the results on real data show that the accuracies of PPI prediction for yeast data and
human data measured as AUC are increased by up to 19 % and 11 % respectively, as compared to a control method
without using optimal weights. Moreover, the weights learned by our method Weight Optimization by Linear
Programming (WOLP) are very consistent with that learned by sampling, and can provide insights into the relations
between PPIs and various feature kernel, thereby improving PPI prediction even for disconnected PPI networks.
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Huang, Lei, Li Liao, and Cathy H. Wu. "Protein-protein interaction prediction based on multiple kernels and partial network with linear programming." BMC Systems Biology 10.2 (2016): 45.