Information assessment on predicting proteinprotein interactions
 Nan Lin^{1},
 Baolin Wu^{2},
 Ronald Jansen^{3},
 Mark Gerstein^{4, 5} and
 Hongyu Zhao^{6, 7}Email author
https://doi.org/10.1186/147121055154
© Lin et al; licensee BioMed Central Ltd. 2004
Received: 31 May 2004
Accepted: 18 October 2004
Published: 18 October 2004
Abstract
Background
Identifying proteinprotein interactions is fundamental for understanding the molecular machinery of the cell. Proteomewide studies of proteinprotein interactions are of significant value, but the highthroughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to highthroughput experimental data, many diverse types of genomic data can help predict proteinprotein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between proteinprotein interactions and other genomic information.
Results
Our assessment is based on the genomic features used in a Bayesian network approach to predict proteinprotein interactions genomewide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functionalclassification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions.
Conclusions
In the restricted problem posed by the completeinformation subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the proteinprotein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.
Background
Proteins transmit regulatory signals throughout the cell, catalyze large numbers of chemical reactions, and are crucial for the stability of numerous cellular structures. Interactions among proteins are key for cell functioning and identifying such interactions is crucial for deciphering the fundamental molecular mechanisms of the cell. As relevant genomic information is exponentially increasing both in quantity and complexity, in silico predictions of proteinprotein interactions have been possible but also challenging. A number of techniques have been developed that exploit combinations of protein features in training data and can predict proteinprotein interactions when applied to novel proteins. Our study is motivated by a study by Jansen et al. [1], who proposed a Bayesian method to use the MIPS [2] complexes catalog as gold standard positives and lists of proteins in separate subcellular compartments [3] as gold standard negatives. The various protein features considered in this method include time course mRNA expression fluctuations during the yeast cell cycle [4] and the Rosetta compendium [5], biological function data from the Gene Ontology [6] and the MIPS functional catalog, essentiality data [2], and highthroughput experimental interaction data [7–10]. The MIPS and Gene Ontology functional annotations are used for quantifying the functional similarity between two proteins. The MIPS functional catalog (or GO biological process annotation) can be thought of as a hierarchical tree of functional classes (or a directed acyclic graph (DAG) in the case of GO). Each protein is either a member or not a member of each functional class, such that each protein describes a "subtree" of the overall hierarchical tree of classes (or subgraph of the DAG in the case of GO). Given two proteins, one can compute the intersection tree of the two subtrees associated with these proteins. This intersection tree can be computed for the complete list of protein pairs (where both proteins of each pair are in the functional classification), and thus a distribution of intersection trees is obtained. Then the "functional similarity" between two proteins is defined as the frequency at which the intersection tree of the two proteins occurs in the distribution. Intuitively, the intersection tree gives the functional annotation that two proteins share. The more ubiquitous this shared functional annotation is, the larger is the functional similarity frequency; the more specific the shared functional annotation is, the smaller is the functional similarity frequency. The essentiality data represents a categorical variable that denotes whether zero, one or both proteins in a protein pair are essential. The supplementary online material of [1]http://www.sciencemag.org/cgi/data/302/5644/449/DC1/1 provides more details about the quantification of these variables. Their Bayesian method predicts proteinprotein interactions genomewide by probabilistic integration of genomic features that are weakly associated with interactions (mRNA expression, essentiality and localization). The model was used for two separate predictions of probabilistic interactomes (PI), one of which (PIE) is built on four highthroughput experimental interaction data sets, and the other (PIP) on the mRNA expression, Gene Ontology, MIPS functional and coessentiality data. Within the PIP subnetwork, different genomic features are assumed to be independent in prior. In addition, this method involved discretizing the raw data into groups and representing the two mRNA expression profiles (cell cycle and Rosetta compendium data) by their first principal component for computational convenience.
Our current study focuses on assessing the contributions of different types of genomic data towards predicting proteinprotein interactions. This may help us to understand which genomic features have the closest biological relationship with proteinprotein interactions and hence to construct a better prediction model. As prediction rules involving less relevant information may have lower prediction accuracy, our analysis can give us insights into how to construct more parsimonious models with comparable or better prediction accuracy. A potential disadvantage of the Bayesian network approach may be that the data discretization can obscure information contained in the raw genomic data. Thus, in addition to assessing the information content of the data sources, we also propose alternative nonBayesian models that fully utilize the data without discretization. These methods, such as logistic regression and random forests, do not require prior knowledge, and we can evaluate the importance of the different genomic features in the context of these methods.
Results and discussions
To accurately and quantitatively assess the information contributions of different genomic features, we construct in essence a simplified problem that has some but not all of the elements of the original study. Here, we only look at a subset of the data from [1] comprising the 18 million protein pairs in total and approximately 8,000 gold standard positives and 2.7 million gold standard negatives. This subset (see Additional File 1) contains 2,104 positives and 172,409 negatives. In this subset, we have complete information for each feature and we can thus quantitatively assess the relative contributions of the different features on this set. This data set can be downloaded from http://bioinformatics.med.yale.edu/PPI. In doing so, we find that some of the features have stronger influence on the overall prediction. While this might be true for the larger problem as well, there are a number of caveats that one has to keep in mind, such as that the features that are present in this subset might not be the strongest in the whole set of 18 million protein pairs.
Alternative models
Here, we construct models for predicting proteinprotein interactions that, given the gold standards, are basically dichotomous classifiers. Multiple logistic regression [11] is one commonly used model for such an application [12, 13]. An alternative, more sophisticated supervised learning approach that we apply is the random forest algorithm [14]. Note that, although not our focus here, all these methods can be used to compute the estimated probabilities for predicted proteinprotein interactions.
Logistic regression

2(log L_{1}  log L_{0}),
where L_{0} is the likelihood of the final model given by the stepwise selection, and L_{1} is the likelihood of the reduced model by removing all terms that involve the corresponding predictor variable from the final model. However, this measure only considers the prediction power of variables for the training sample but not for any random test samples. Therefore, this measure can be biased due to its dependence on the training sample.
Order of variables that enter the final model by stepwise selection in logistic regression
Variables  Order 

Gavin  1 
MIPS  2 
Rosetta  3 
GO  4 
cellcycle  5 
essentiality  6 
Rosetta*cellcycle  7 
cellcycle*essentiality  8 
Ho  9 
GO*essentiality  10 
Uetz  11 
GO*cellcycle  12 
GO*cellcycle*essentiality  13 
MIPS*essentiality  14 
MIPS*Rosetta  15 
Deviance of the reduced model from the final model by removing corresponding variables
Variable  Deviance 

GO  1376.437 
MIPS  1333.97 
essentiality  579.988 
Rosetta  778.493 
cellcycle  1271.461 
Ho  68.718 
Uetz  20.513 
Gavin  1839.181 
Random forest
The "random forest" method [14] is a supervised learning algorithm that has previously been successfully applied to many genomic studies. It has been implemented in the randomForest package of R [15]. A random forest is an ensemble of many classification trees generated from bootstrap samples of the original data. It is well known that random forests avoid overfitting and usually have better classification accuracy than classification trees. A natural way to evaluate the importance of the feature variables with the random forest algorithm is to measure the increase of the classification error when those variables are permuted. Intuitively, the more important variables will, when permuted, produce larger classification errors. The importance score provided by the random forest is a more accurate estimate of the classification error that considers the situation of random test samples. Therefore, this importance score provides a more objective evaluation of the relative merit of different genomic features on proteinprotein interaction prediction. Moreover, the intrinsic tree structure of the random forest easily takes into account the interactions among the different variables and avoids complications caused by missing data that occurred in many other modeling procedures.
Comparison of three methods
Information assessment
Information assessment of different genomic data may help us understand their relationship with proteinprotein interactions, and form a guideline for future model development.
MIPS and gene ontology functional similarity data
In the following paragraphs, we show quantitatively that the MIPS and Gene Ontology functional similarities are the dominating information contributors for predicting proteinprotein interactions, while other genomic features have negligible benefit and can not provide credible predictions by themselves. We examine the performance of random forests using three different genomic feature sets: (i) all genomic features included, (ii) MIPS and Gene Ontology functional similarities only, and (iii) genomic features other than the MIPS and Gene Ontology functional similarities. The random forest performance is evaluated with the classification error (Err) defined as follows.
Denote Err_{1} as the proportion of protein pairs misclassified in the gold standard positives, and Err_{2} the counterpart for the gold standard negatives. Then we define the classification error as the average of Err_{1} and Err_{2}.
Err is a balanced error rate across gold standard positives and negatives. Suppose the joint probability density functions of the predictor features X are f_{1}(X) and f_{2}(X) for the gold standard positives and negatives, respectively. Denote a classifier by C(X). Then the classification error can be written as
where I(A) is an indicator function equal to 1 when A is true and 0 otherwise. A minimal classification error Err_{ min }can be computed by minimizing (1) across the space of X. It is easy to see that
is achieved at C(X) = I(f_{1}(X) >f_{2}(X)). With this formula, we can estimate the optimal (minimum) classification error based on any estimates of f_{1}(X) and f_{2}(X). In our study, f_{1}(X) and f_{2}(X) are estimated by their empirical density functions.
Optimal classification errors when using different genomic features
Variables  Optimal Classification Error 

MIPS  1.69% 
GO  2.15% 
MIPS+GO  0.28% 
MIPS (grouped)  7.31% 
GO (grouped)  13.35% 
MIPS+GO (grouped)  6.34% 
Other genomic features
Classification errors of the random forest algorithm when using different genomic features
Variables  Err_{1} (positives)  Err_{2} (negatives)  Err 

MIPS+GO  114/2104 = 5.42%  180/172409 = 0.1%  2.76% 
ALL  165/2104 = 7.80%  89/172409 = 0.05%  3.95% 
ELSE  1056/2104 = 78.09%  313/172409 = 0.20%  25.20% 
Conclusions
In the restricted problem posed by the completeinformation subset, we identified that the MIPS and Gene Ontology functional similarity datasets as the dominating information contributors for predicting the proteinprotein interactions under the framework proposed in [1]. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. The MIPS and GO information, however, is only available for a small proportion of the ~18M protein pairs.
We considered alternative nonBayesian methods such as logistic regression and random forest for predicting proteinprotein interactions. These existing methods do not require prior information needed for the Bayesian approach, and can fully utilize the raw data without discretization. The logistic model performs similarly as the Bayesian method in terms of classifications and, like the Bayesian method, produces estimated probabilities that two proteins interact. As a dichotomous classifier, the random forest method outperforms the other methods considered and efficiently uses the information, although it is computationally more expensive. In particular, its importance measure provides a more objective assessment of different genomic features on predicting proteinprotein interactions than simply considering contributions to model fitting. These findings are motivation to look for other, more sensible data resources and superior models.
We found that the data discretizations used in the Bayesian methods decreased classification performance. We note here that the genomic features datasets investigated here themselves are highly processed versions of the datasets they were derived from and that there may be better ways to take the original data into account.
Another caveat is that the predictions might be just defining groups of proteins that have the same genomic properties as the protein complexes in the MIPS data. This does not necessarily mean that they really represent protein complexes. Rather, they may represent groups of proteins that have the same properties as protein complexes.
In this analysis we have looked at the relative weights of various features in predicting proteinprotein interactions based on the previous study in [1]. We looked at a particular subset of the data where we had complete information and we were able to show that, for this particular subset of the full information, we are able to show that the functional classification features in the MIPS functional catalog and Gene Ontology were the most informative and that particular machine learning algorithms, such as random forests were more effective than Bayesian networks. However, one has to keep in mind that in the full problem there is the issue of incomplete information. On data sets with incomplete information Bayesian approaches maybe more effective because they can easily handle the missing information. Further careful studies such as these will be needed to determine what the optimum machine learning method is and the optimum features are in presence of incomplete information. It will be also of great interest to consider other genomic features such as phylogenetic profiles [16] and local clustering information [17]. This is just the first step in that direction.
Methods
Logistic regression
Denote the gold standards by random variable Y and the other genomic features by X_{1}, X_{2}, ..., X_{ n }. Let Y = 1 when two proteins interact, i.e., they are in the same complex, and Y = 0 when not. The logistic model is of the form
where the random vector X consists of X_{1}, X_{2}, ..., X_{ n }and their interaction terms.
Stepwise variable selection
The stepwise selection procedure starts from a null model. At each step, it adds a variable with the most significant score statistics among those not in the model, then sequentially removes the variable with the least score statistic among those in the model whose score statistics are not significant. The process terminates if no further variable can be added to the model or if the variable just entered into the model is the only variable removed in the subsequent elimination. Here, the score statistic measures the significance of the effect of a variable.
ROC curve analysis
Receiving operator characteristic (ROC) curve [18] is a graphical representation used to assess the discriminatory ability of a dichotomous classifier by showing the tradeoffs between sensitivity and specificity. Sensitivity is calculated by dividing the number of true positives (TP) through the number of all positives, which equals the sum of the true positives and the false negatives (FN); specificity is calculated by dividing the number of true negatives (TN) through the number of all negatives, which equals the sum of the true negatives and the false positives (FP).
Sensitivity = TP/(TP + FN), Specificity = TN/(TN + FP).
The ROC curve plot shows 1  specificity on the X axis and sensitivity on the Y axis. A good classifier has its ROC curve climbing rapidly towards upper left hand corner of the graph. This can also be quantified by measuring the area under the curve. The closer the area is to 1.0, the better the classifier is; and the closer the area is to 0.5, the worse the classifier is.
Declarations
Acknowledgements
This work was supported in part by NSF grant DMS 0241160 and NIH grant GM 59507.
Authors’ Affiliations
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