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

Qi Y, Klein-Seetharaman J, Bar-Joseph Z - BMC Bioinformatics (2007)

Bottom Line: High-throughput methods can directly detect the set of interacting proteins in model species but the results are often incomplete and exhibit high false positive and false negative rates.However, due to missing data and high redundancy among the features used, different protein pairs may benefit from different features based on the set of attributes available.Our method improved upon the best previous methods for this task.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. qyj@cs.cmu.edu

ABSTRACT

Background: High-throughput methods can directly detect the set of interacting proteins in model species 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. These methods utilize a common classifier for all pairs. However, due to missing data and high redundancy among the features used, different protein pairs 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 data sources contributed to a prediction. This information is important for biologists using these predications in the design of new experiments.

Results: To address these challenges we propose a Mixture-of-Feature-Experts method for protein-protein interaction prediction. We split the features into roughly homogeneous sets of feature experts. The individual experts use logistic regression and their scores are combined using another logistic regression. When combining the scores the weighting of each expert depends on the set of input attributes available for that pair. Thus, different experts will have different influence on the prediction depending on the available features.

Conclusion: We applied our method to predict the set of interacting proteins in yeast and human cells. Our method improved upon the best previous methods for this task. In addition, the weighting of the experts provides means to evaluate the prediction based on the high scoring features.

Show MeSH

Related in: MedlinePlus

Converting Data Sources to A Feature Vector Representing Each Protein-Protein Pair. The process of combining biological sources and then converting them to feature vectors describing protein-protein pairs. For a gene/protein specific feature, we found a natural way to transform it to represent the protein-protein pair. For example, for gene expression data, we use the correlation coefficient as the feature for a protein-protein pair.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2230507&req=5

Figure 1: Converting Data Sources to A Feature Vector Representing Each Protein-Protein Pair. The process of combining biological sources and then converting them to feature vectors describing protein-protein pairs. For a gene/protein specific feature, we found a natural way to transform it to represent the protein-protein pair. For example, for gene expression data, we use the correlation coefficient as the feature for a protein-protein pair.

Mentions: We present the converting process briefly in Figure 1. For each data set that represents a certain gene/protein's property, we figured out one natural way to calculate the similarity between two genes/proteins with respect to the specific evidence. For instance, for two proteins' sequence information, we use BlastP [27] sequence alignment E-value as one feature for this protein-protein pair from the protein sequence evidence. For other data sources, similar procedures were pursued to make the features for a protein pair. For data sets directly describing a protein/gene pair, we used them directly as features, like synthetic lethal evidence. Concatenating all these features together then gave us the feature vector describing a protein-protein pair.


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

Qi Y, Klein-Seetharaman J, Bar-Joseph Z - BMC Bioinformatics (2007)

Converting Data Sources to A Feature Vector Representing Each Protein-Protein Pair. The process of combining biological sources and then converting them to feature vectors describing protein-protein pairs. For a gene/protein specific feature, we found a natural way to transform it to represent the protein-protein pair. For example, for gene expression data, we use the correlation coefficient as the feature for a protein-protein pair.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2230507&req=5

Figure 1: Converting Data Sources to A Feature Vector Representing Each Protein-Protein Pair. The process of combining biological sources and then converting them to feature vectors describing protein-protein pairs. For a gene/protein specific feature, we found a natural way to transform it to represent the protein-protein pair. For example, for gene expression data, we use the correlation coefficient as the feature for a protein-protein pair.
Mentions: We present the converting process briefly in Figure 1. For each data set that represents a certain gene/protein's property, we figured out one natural way to calculate the similarity between two genes/proteins with respect to the specific evidence. For instance, for two proteins' sequence information, we use BlastP [27] sequence alignment E-value as one feature for this protein-protein pair from the protein sequence evidence. For other data sources, similar procedures were pursued to make the features for a protein pair. For data sets directly describing a protein/gene pair, we used them directly as features, like synthetic lethal evidence. Concatenating all these features together then gave us the feature vector describing a protein-protein pair.

Bottom Line: High-throughput methods can directly detect the set of interacting proteins in model species but the results are often incomplete and exhibit high false positive and false negative rates.However, due to missing data and high redundancy among the features used, different protein pairs may benefit from different features based on the set of attributes available.Our method improved upon the best previous methods for this task.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA. qyj@cs.cmu.edu

ABSTRACT

Background: High-throughput methods can directly detect the set of interacting proteins in model species 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. These methods utilize a common classifier for all pairs. However, due to missing data and high redundancy among the features used, different protein pairs 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 data sources contributed to a prediction. This information is important for biologists using these predications in the design of new experiments.

Results: To address these challenges we propose a Mixture-of-Feature-Experts method for protein-protein interaction prediction. We split the features into roughly homogeneous sets of feature experts. The individual experts use logistic regression and their scores are combined using another logistic regression. When combining the scores the weighting of each expert depends on the set of input attributes available for that pair. Thus, different experts will have different influence on the prediction depending on the available features.

Conclusion: We applied our method to predict the set of interacting proteins in yeast and human cells. Our method improved upon the best previous methods for this task. In addition, the weighting of the experts provides means to evaluate the prediction based on the high scoring features.

Show MeSH
Related in: MedlinePlus