A mixture of feature experts approach for protein-protein interaction prediction

High-throughput methods can directly detect the set of interacting proteins in yeast but the results are often incomplete and exhibit high false positive and false negative rates. A number of researchers have recently presented methods for integrating direct and indirect data for predicting interactions. However, due to missing data and the high redundancy among the features used, different samples may benefit from different features based on the set of attributes available. In addition, in many cases it is hard to directly determine which of the datasets led to the prediction, which is an important issue for the biologists using these predications to design new experiments. To address these challenges we use a Mixture-of-Experts method. We split the data into four (roughly) homogeneous sets. The individual experts use logistic regression and their scores are combined using another logistic regression. However, when combining the scores the weighting of each expert depends on the s...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Yanjun Qi, Judith Klein-Seetharaman, Ziv Bar-Joseph
• Keywords: BMCBI 2007, False Negative Rates, False Positive, Logistic Regression