Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background#R##N#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. Hover, how to quickly learn ights for heterogeneous data sources remains a challenge. In this work,  developed a method to infer de novo PPIs by combining multiple data sources represented in kernel format and obtaining optimal ights based on random walk over the existing partial networks.
['Lei Huang', 'Li Liao', 'Cathy H. Wu']
I am writing a research paper on a idea Protein-protein interaction prediction based on multiple kernels and partial network with linear programming
[{"from": "human", "value": "I am writing a research paper on a idea Protein-protein interaction prediction based on multiple kernels and partial network with linear programming"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background#R##N#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. Hover, how to quickly learn ights for heterogeneous data sources remains a challenge. In this work,  developed a method to infer de novo PPIs by combining multiple data sources represented in kernel format and obtaining optimal ights based on random walk over the existing partial networks."}]