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Protein-protein interaction prediction based on multiple kernels and partial network with linear programming
- Source :
- BMC Systems Biology
- Publication Year :
- 2016
- Publisher :
- BioMed Central, 2016.
-
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.
- Subjects :
- 0301 basic medicine
Theoretical computer science
Linear programming
Computer science
0206 medical engineering
02 engineering and technology
Random walk
Synthetic data
Network inference
03 medical and health sciences
Robustness (computer science)
Structural Biology
Modelling and Simulation
Feature (machine learning)
Interaction prediction
Adjacency matrix
Subnetwork
Molecular Biology
Applied Mathematics
Research
Computer Science Applications
Protein interaction network
030104 developmental biology
Modeling and Simulation
Kernel (statistics)
Protein–protein interaction prediction
Algorithm
020602 bioinformatics
Subjects
Details
- Language :
- English
- ISSN :
- 17520509
- Volume :
- 10
- Issue :
- Suppl 2
- Database :
- OpenAIRE
- Journal :
- BMC Systems Biology
- Accession number :
- edsair.doi.dedup.....63155357d157437e7f79a9cece40f310