A Mixture of Experts
Approach for Protein-Protein Interaction Prediction Yanjun Qi1, Judith Klein-Seetharaman1,2, Ziv Bar-Joseph1 1School of Computer Science, 2Department of Pharmacology, |
Abstract 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 {\em 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 lead 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 set of input attributes. Thus different experts will have different influence on the prediction depending on the available features. We applied our method to predict the set of interacting proteins in yeast. Our method improved upon the best previous methods for this task. In addition, using the weighting of the experts the prediction can be easily evaluated by biologists based on the features that they feel are the most reliable. |
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Features Used ·
Features
used in the paper are described in detail in the following paper o
Y. Qi, Z. Bar-Joseph, J. Klein-Seetharaman, "Evaluation of different biological data and
computational classification methods for use in protein interaction
prediction", PROTEINS:
Structure, Function, and Bioinformatics. Jan 2006 o
This
paper’s supplementary website
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Performance Comparison ·
The
performance comparison was done by the random sampled Train set / Test set
style ·
We used two measures to evaluate performance o
Precision vs. Recall curves o
R50 partial area under Receiver Operator
Characteristic scores. o
For detailed description about these two
criterions, please reference to o
Y. Qi, Z. Bar-Joseph, J. Klein-Seetharaman, "Evaluation of different biological data and
computational classification methods for use in protein interaction
prediction", PROTEINS:
Structure, Function, and Bioinformatics. ( In Press ) ·
Due
to the space limit, we just put the R50 comparison in the paper ·
Here
we present the precision vs. recall curves between these 5 methods. From this
plot, we could also find that the feature experts based method still is
favorable. |