BACKGROUND
protein-protein interactions  are the foundation of most biological mechanisms such as dna replication and transcription, enzyme-mediated metabolism, signal transduction, and cell cycle control  <cit> .
therefore, information on the physiological interactions of proteins is perhaps one of the most valuable resources from which annotations of genes and proteins can be discovered.
traditional biology approach studies protein-protein interactions individually by low-throughput technologies  <cit> .
in more recent "high-throughput" view, protein interactions are visualized as a sophisticated network and studied globally with technologies such as yeast two-hybrid system  <cit> , affinity purification followed by mass spectrometry  <cit> , protein chips  <cit> , gel-filtration chromatography  <cit> , and phase display  <cit> .
these high-throughput genome-wide protein interaction screens have been carried out in many organisms and produced thousands of experimentally identified protein-protein interactions.
one major issue, however, is the prevalence of spurious interactions in the high-throughput interaction data.
errors may arise from a wide range of affinities and timescales by which proteins interact with one another.
analysis by deane et al.
<cit>  suggests that only 30–50% of the high-throughput interactions are biologically relevant.
in an independent study, mrowka et al.
<cit>  observed significant difference in individually identified interactions from those by genome-wide scans, and estimated that some whole-genome scans may contain 44–91% of false positives.
these false positives, i.e.
interactions that are detected in the experiment but never take place in the cell, may connect unrelated proteins in the interaction network, create unnecessary interaction clusters, and incorrect biological conclusions may be drawn as a consequence.
hence, to effectively use the high-throughput data in biological inferences, it is critical to evaluate the quality of the data and remove as many false positive interactions as possible.
various approaches have been proposed to analyze the proteomics data by extracting the subset of valid interactions from their background noise.
in some original high-throughput experiments  <cit> , promiscuity criteria are employed to remove proteins having many interaction partners.
one limitation of this method is that it can only be applied ad hoc because there is no clear separation between the 'sticky'  and 'non-sticky'  proteins.
moreover, biological networks are scale-free in nature  <cit> , which implies that the highly connected proteins may as well be a real feature of the protein interaction networks.
on the other hand, two independent analyses by von mering et al.
<cit>  and bader and hogue  <cit>  studied intersections between different high-throughput datasets and demonstrated that interaction pairs identified by multiple experiments are enriched in true interactions.
a shortcoming of this method is the lack of overlap between datasets.
not only data from different technologies do not overlap significantly, but also data from different labs using the same technology differ substantially.
this suggests that the current data are far from saturating, and data from different resources are complementary to each other.
it is also possible to explore the relationship between protein-protein interaction data and other types of data to assess the quality.
mrowka et al.
<cit>  compared distributions of transcription correlations between the interaction data from many single hypothesis-driven experiments and genome-wide scans.
using data from the munich information center for protein sequences   <cit>  as the reference set of true interactions, they described a bootstrap method to count how many random pairs needed to be inserted in order to create the same statistical behaviour of the expression correlation as in the putative interaction data.
other colleagues applied microarray and mrna expression data to assess the quality of protein-protein interaction data  <cit> .
nevertheless, interacting proteins do not necessarily display correlation in mrna levels.
in fact, proteins in a permanent complex may even show low transcriptional correlation due to differences in degradation rates  <cit> .
even worse, bader et al.
<cit>  noticed that for the data from mass spectrometry of coimmunoprecipitated protein complexes , the correlated coexpression may be negatively correlated with predicted interaction confidence.
besides expression data, sequence homology between two proteins and their corresponding interaction partners has been adopted to verify high-throughput protein-protein interactions  <cit> .
however, the verification process is restricted to interaction pairs with both proteins having homologs, and even for these applicable interaction pairs, only half are identified as high confident under the homology criterion  <cit> .
moreover, other groups made use of cellular localization and cellular-role properties to assess the reliability of high-throughput experimental data  <cit> .
furthermore, saito et al.
<cit>  and goldberg and roth  <cit>  exploited network topological descriptors to determine how well an edge  fits the expected topology of protein-protein interaction network.
altogether, the aforementioned methods apply threshold values to assess the quality of interactions by classifying them as either high or low confidence.
likewise, a number of computational approaches for protein interaction prediction have been developed to designate two proteins as either interacting or not interacting based on genomic context  <cit>  and protein domain  <cit> .
despite their varying successes, it is much more beneficial to estimate the probability that a pair of proteins may form a true interaction rather than producing a binary outcome.
recently, there has been a growing interest in data integration.
in a study on the yeast signal transduction pathway for amino acid transport, chen and xu  <cit>  demonstrated that integration of high-throughput data with other biology resources can transform the noisy protein interaction data into useful knowledge.
many probabilistic methods have explored the integration of complementary data sources for protein interaction inference, which turned out to improve both accuracy and coverage.
integrating diverse types of evidences such as gene expression, gene ontology   <cit> , and enriched domain pairs, research groups have proposed probabilistic decision tree  <cit> , logistic regression  <cit> , naïve bayes  <cit> , and bayesian network  <cit>  models.
in this study, we describe a novel bayesian network-based integrative model that assigns a likelihood score to every interaction.
the main contributions we make are as follows.
first, we establish a cross-species in silico model to assess confidence of two proteins to interact in a target organism  on the basis of information entirely extracted from other model organisms .
a cross-organism computational system for protein interaction prediction is attractive and needed, mainly because model organisms are well studied and have a tremendous amount of experimental data, while there may be little information about the target organism, especially with newly sequenced proteins .
among protein interaction studies using data from model organisms, data from target organism is employed in addition to data from other organisms  <cit> .
in existing integrative models, data from model organisms may not even play a significant role.
for instance, rhodes et al.
<cit>  showed that information from model organisms alone is only moderately predictive.
thus, there is an essential need for better probabilistic models that can effectively integrate heterogeneous data sources from model organisms.
our proposed model demonstrates that a carefully designed system is capable of making accurate assessment utilizing information solely from model organisms.
second, we introduce a novel bayesian network-based approach to integrate multiple microarray datasets and go information.
in contrary to commonly used naïve bayes model, we do not make conditional independence assumption among multiple microarray datasets and new features extracted from go biological processes, molecular functions, and cellular components.
furthermore, the confidence scores for orthologous mappings are explicitly incorporated into our model.
finally, applying our cross-species in silico model, we assess the overall quality of the protein-protein interaction data obtained from high-throughput screens for yeast.
RESULTS
system overview
the proposed cross-organism predictive system is illustrated in figure  <dig>  for a pair of proteins  in a target organism, genome-wide orthologous mapping between the target organism and model organisms can be obtained from the inparanoid database  <cit> .
the inparanoid program uses ncbi-blast to calculate the pairwise similarity scores between two complete proteomes.
a confidence value c is then provided to evaluate how closely related two orthologs are.
our strategy is as follows.
first, for a pair of proteins , we determine their orthologs in the model organisms.
second, features are extracted for each ortholog pair from gene expression profiles and go annotations of model organisms .
finally, the heterogeneous data features are integrated to describe the protein pair  of the target organism using a bayesian network-based model  that assigns likelihood ratios for interaction.
novel feature extraction
to determine how likely two proteins will interact, several features are derived from gene expression profiles and go annotations.
for each protein pair  in the target organism, we identify its ortholog pairs  <dig>  r2) in three model organisms .
from each model organism, we download three microarray datasets.
for each ortholog pair  <dig>  r2), pearson correlation coefficients  are calculated from the gene expression profiles.
a 4-level uniform quantization is used and each pcc is discretized into one of four states: high, medium high, medium low, and low.
rather than assuming the pccs extracted from different microarray data are independent, we model the three pccs from individual model organism jointly with one node in our bayesian network model .
this is very different from the commonly-used naïve bayes method in which every feature is assumed to be independent of each other.
moreover, we derive novel features from go annotations for each identified ortholog pair  <dig>  r2) of the protein pair .
three unique features are derived from each of the "molecular function", "biological process", and "cellular component" annotations in go.
the first feature checks whether two proteins share annotation terms: if the two proteins share at least one common term, the feature value is one; otherwise, it is zero.
the second feature is called correlation ratio.
in go, gene products can be associated with more than one term.
therefore, the correlation between two go terms is defined as the number of gene products in common.
the larger the correlation value is, the closer the two go terms are.
we examine all possible pairs of go terms between the two proteins and identify two go terms  with the largest correlation value.
the correlation ratio is then defined as n/, where n is the correlation between term_ <dig> and term_ <dig>  n <dig> and n <dig> are the numbers of gene products with term_ <dig> and term_ <dig>  respectively.
the correlation ratio is also quantized into two levels with a threshold of  <dig> : high and low.
the third feature is based on the minimum go distance d between two proteins.
since go is organized as a directed acyclic graph where each node represents a go term, distance between two terms is described as the least number of nodes separating them in the graph.
again, to identify the two go terms  with the minimum distance, we examine all possible pairs of go terms between the two proteins.
for the third feature, incorporating the graph structures, we define eight states:  <dig> if d is zero;  <dig> if d is one ;  <dig> if d is two and term_ <dig> and term_ <dig> have a grandparent-children relationship ;  <dig> if d is two and term_ <dig> and term_ <dig> are siblings with a common parent term ;  <dig> if d is three and term_ <dig> is term_4's great grandparent or vice versa ;  <dig> if d is three and term_3's parent and term_ <dig> are siblings with a common parent term ;  <dig> if d is three with all remaining graph structural cases; and  <dig> if d is larger than three.
apparently, the three features are not independent to each other.
in our integrative system, we model the three features from each model organism jointly with one node.
human protein-protein interaction prediction
it is important to investigate how widely applicable our approach is for automatic verification of large sets of interactions.
if a method is sufficient, its predicted protein-protein interactions  should have higher overlap with the previously established interactions.
to evaluate our integrative method, we use both specificity and sensitivity.
the specificity is defined as the percentage of matched non-interactions between the predicted set and the observed set over the total number of observed non-interactions.
the sensitivity is defined as the percentage of matched interactions over the total number of observed interactions.
first of all, with a specificity of 95%, our method can achieve a sensitivity of about 44%, and if the specificity reduces to 50%, the sensitivity increases to 80%.
these results clearly demonstrate that model organisms certainly provide significant information for the prediction of ppis in the target organisms.
secondly, we compare our method to the commonly-used naïve bayesian method  <cit> .
in the naïve bayesian model, all the features are assumed to be conditionally independent, i.e., features extracted from three microarray data sets and three novel features from go in each model organism are conditionally independent given ppi.
table  <dig> contains results of our method and the naïve bayesian method over the test dataset.
with comparable specificities fixed at approximately the same level 70%, our method can achieve 73% in sensitivity and the naïve bayesian can only reach 65% in sensitivity.
assessment of yeast protein-protein interaction data
while high-throughput technologies generate thousands of protein-protein interactions  data and allow for genome-wide analysis, they tend to produce a large number of false positives.
on the other hand, low-throughput methods can yield reliable results but are typically labor intensive, time consuming, and on a small scale basis.
computational methods provide an ideal tool for evaluating experimentally detected ppis, as in silico methods can  utilize existing biological knowledge;  predict large-scale ppis; and  produce the confidence levels of interactions for each protein pairs.
we apply our cross-organism integrative in silico model to evaluate high-throughput yeast ppi data and detect the spurious interactions.
our model is ideal for this type of application, as we do not use the direct ppi data from model organisms in our training process .
the current available yeast interaction pairs in databases may be determined by various experiments; therefore, the more experiments confirming it, the more confident we are in the interaction.
we collected the yeast interaction data from the general repository for interaction datasets   <cit> .
the deposited interactions are determined through a number of methods, but we mainly focus on four: synthetic lethality, affinity capture-ms, two-hybrid, and phenotypic enhancement.
total number of ppis detected by each of the experimental methods are  <dig>   <dig>   <dig>  and  <dig> for synthetic lethality, affinity capture-ms, two-hybrid, and phenotypic enhancement, respectively.
among the four datasets, total number of unique pairs is  <dig>  therefore, there are  <dig> overlapping pairs between the datasets.
because our goal is to analyze the system on ppis determined by different number of experiments, four data files are generated, in which one contains interaction pairs identified by only one experiment, another contains pairs from only two experiments, etc.
finally, there are  <dig>   <dig>   <dig>  and  <dig> ppis identified by one, two, three, and four different experiments, respectively.
each ppi pair can be ranked by the likelihood ratio .
the larger the ratio is, the higher confidence we have in the interactions.
we consider a protein pair as interacting if its likelihood ratio is larger than one .
figure  <dig> shows the percentage of ppis detected by high-throughput methods that are also predicted by our cross-organism model.
as can be seen, all the ppis that are supported by four different experiments are also predicted as positives by our model.
we also predict 44%, 64%, and 97% of ppis detected by one, two, and three biological experiments as interacting protein pairs, respectively.
notably, the percent of true positives  for ppis with only one experimental evidence is similar to the positive rate estimated by sprinzak et al.
<cit> .
moreover, we analyze some ppis detected by high-throughput experiments but predicted as negatives by our model.
to assess the data, we consider the shortest distance of two proteins in go cellular components, molecular function, and biological process.
as discussed by sprinzak et al.
<cit> , for true interactions, the interacting proteins should be localized in the same cellular compartment or participate in the same cellular process.
the protein pair, yjl179w and ybr258c, is identified by one high-throughput experiment but predicted as non-interacting by our method.
the closest cellular component terms between yjl179w and ybr258c are go: <dig>  and go:  <dig> .
as can be seen in figure  <dig>  these two proteins are not localized to the same compartment.
we can also observe that the two proteins do not participate in the same cellular process  nor execute the same function .
similar observation can be made regarding protein pairs supported by two biological experiments but predicted as non-interacting.
for example, the protein pair, yel061c and ynl147w, has several pairs of go cellular component terms that are closest to each other between the two proteins: , , , and  .
go: <dig> refers to a multisubunit complex that is located at the pericentric region of a condensed chromosome in the nucleus and provides an attachment point for the spindle microtubules.
go: <dig> describes a semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells.
it is notably the site of tissue respiration.
go: <dig> represents a complex composed of rna of the small nucleolar rna  and protein, found in the nucleolus of a eukaryotic cell.
go: <dig> refers to the ribonucleoprotein complex containing small nuclear rna u6; a component of the major spliceosome complex.
go: <dig> refers to a complex composed of three small nuclear ribonucleoproteins, snrnp u <dig>  snrnp u <dig>  and snrnp u <dig>  figures  <dig> and  <dig> illustrate the closest biological process and molecular function terms, respectively.
CONCLUSIONS
the advent of high-throughput technologies has significantly enlarged the collection of protein-protein interactions.
on one hand, it has provided a rich source of information for new biological discoveries.
on the other hand, it has introduced a technical challenge due to its high error rates.
it has been shown by many researchers that the reliability of high-throughput screens is only about 50%.
the large number of false positives may result in false biological conclusions.
it is thus essential to assess the quality of the interactions.
in this paper, we develop a novel bayesian network-based model that integrates heterogeneous data sources from model organisms to determine the probability of two proteins to interact in a target organism.
cross-species prediction is attractive as normally we do not have much information about newly sequenced proteins.
by mapping them to well studied model organisms; however, we are able to utilize the existing biological knowledge of the model organisms to make accurate predictions.
our model is successfully applied to predict protein-protein interactions in human.
for the protein pairs with orthologous mappings in all three model organisms, our model can achieve 80% in sensitivity and 70% in specificity.
the method is also successfully applied to assess the quality of the high-throughput interaction data.
we observed that the more high-throughput experiments confirming an interaction, the higher the confidence score is assigned by our method.
for the protein pairs confirmed by four different biological experiments, we predicted all of them as interacting.
for the pairs supported by only one experiment, the percentage of true positives we determined is similar to the positive rate estimated by sprinzak et al  <cit> .
the above results demonstrate that model organisms indeed provide important information for protein-protein interaction inference and assessment.
the method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions.
we expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application.

 protein-protein interactions are the foundation of most biological mechanisms such as dna replication and transcription, enzyme-mediated metabolism, signal transduction, and cell cycle control. therefore, information on the physiological interactions of proteins is perhaps one of the most valuable resources from which annotations of genes and proteins can be discovered. traditional biology approach studies protein-protein interactions individually by low-throughput technologies. in more recent "high-throughput" view, protein interactions are visualized as a sophisticated network and studied globally with technologies such as yeast two-hybrid system, affinity purification followed by mass spectrometry, protein chips, gel-filtration chromatography, and phase display. these high-throughput genome-wide protein interaction screens have been carried out in many organisms and produced thousands of experimentally identified protein-protein interactions. one major issue, however, is the prevalence of spurious interactions in the high-throughput interaction data. errors may arise from a wide range of affinities and timescales by which proteins interact with one another. analysis by deane et al. suggests that only 30–50% of the high-throughput interactions are biologically relevant. in an independent study, mrowka et al. observed significant difference in individually identified interactions from those by genome-wide scans, and estimated that some whole-genome scans may contain 44–91% of false positives. these false positives, i.e. interactions that are detected in the experiment but never take place in the cell, may connect unrelated proteins in the interaction network, create unnecessary interaction clusters, and incorrect biological conclusions may be drawn as a consequence. hence, to effectively use the high-throughput data in biological inferences, it is critical to evaluate the quality of the data and remove as many false positive interactions as possible. various approaches have been proposed to analyze the proteomics data by extracting the subset of valid interactions from their background noise. in some original high-throughput experiments, promiscuity criteria are employed to remove proteins having many interaction partners. one limitation of this method is that it can only be applied ad hoc because there is no clear separation between the'sticky' and'non-sticky' proteins. moreover, biological networks are scale-free in nature, which implies that the highly connected proteins may as well be a real feature of the protein interaction networks. on the other hand, two independent analyses by von mering et al. and bader and hogue studied intersections between different high-throughput datasets and demonstrated that interaction pairs identified by multiple experiments are enriched in true interactions. a shortcoming of this method is the lack of overlap between datasets. not only data from different technologies do not overlap significantly, but also data from different labs using the same technology differ substantially. this suggests that the current data are far from saturating, and data from different resources are complementary to each other. it is also possible to explore the relationship between protein-protein interaction data and other types of data to assess the quality. mrowka et al. compared distributions of transcription correlations between the interaction data from many single hypothesis-driven experiments and genome-wide scans. using data from the munich information center for protein sequences as the reference set of true interactions, they described a bootstrap method to count how many random pairs needed to be inserted in order to create the same statistical behaviour of the expression correlation as in the putative interaction data. other colleagues applied microarray and mrna expression data to assess the quality of protein-protein interaction data. nevertheless, interacting proteins do not necessarily display correlation in mrna levels. in fact, proteins in a permanent complex may even show low transcriptional correlation due to differences in degradation rates. even worse, bader et al. noticed that for the data from mass spectrometry of coimmunoprecipitated protein complexes, the correlated coexpression may be negatively correlated with predicted interaction confidence. besides expression data, sequence homology between two proteins and their corresponding interaction partners has been adopted to verify high-throughput protein-protein interactions. however, the verification process is restricted to interaction pairs with both proteins having homologs, and even for these applicable interaction pairs, only half are identified as high confident under the homology criterion. moreover, other groups made use of cellular localization and cellular-role properties to assess the reliability of high-throughput experimental data. furthermore, saito et al. and goldberg and roth exploited network topological descriptors to determine how well an edge fits the expected topology of protein-protein interaction network. altogether, the aforementioned methods apply threshold values to assess the quality of interactions by classifying them as either high or low confidence. likewise, a number of computational approaches for protein interaction prediction have been developed to designate two proteins as either interacting or not interacting based on genomic context and protein domain. despite their varying successes, it is much more beneficial to estimate the probability that a pair of proteins may form a true interaction rather than producing a binary outcome. recently, there has been a growing interest in data integration. in a study on the yeast signal transduction pathway for amino acid transport, chen and xu demonstrated that integration of high-throughput data with other biology resources can transform the noisy protein interaction data into useful knowledge. many probabilistic methods have explored the integration of complementary data sources for protein interaction inference, which turned out to improve both accuracy and coverage. integrating diverse types of evidences such as gene expression, gene ontology, and enriched domain pairs, research groups have proposed probabilistic decision tree, logistic regression, naïve bayes, and bayesian network models. in this study, we describe a novel bayesian network-based integrative model that assigns a likelihood score to every interaction. the main contributions we make are as follows. first, we establish a cross-species in silico model to assess confidence of two proteins to interact in a target organism on the basis of information entirely extracted from other model organisms. a cross-organism computational system for protein interaction prediction is attractive and needed, mainly because model organisms are well studied and have a tremendous amount of experimental data, while there may be little information about the target organism, especially with newly sequenced proteins. among protein interaction studies using data from model organisms, data from target organism is employed in addition to data from other organisms. in existing integrative models, data from model organisms may not even play a significant role. for instance, rhodes et al. showed that information from model organisms alone is only moderately predictive. thus, there is an essential need for better probabilistic models that can effectively integrate heterogeneous data sources from model organisms. our proposed model demonstrates that a carefully designed system is capable of making accurate assessment utilizing information solely from model organisms. second, we introduce a novel bayesian network-based approach to integrate multiple microarray datasets and go information. in contrary to commonly used naïve bayes model, we do not make conditional independence assumption among multiple microarray datasets and new features extracted from go biological processes, molecular functions, and cellular components. furthermore, the confidence scores for orthologous mappings are explicitly incorporated into our model. finally, applying our cross-species in silico model, we assess the overall quality of the protein-protein interaction data obtained from high-throughput screens for yeast.  system overview the proposed cross-organism predictive system is illustrated in figure for a pair of proteins in a target organism, genome-wide orthologous mapping between the target organism and model organisms can be obtained from the inparanoid database. the inparanoid program uses ncbi-blast to calculate the pairwise similarity scores between two complete proteomes. a confidence value c is then provided to evaluate how closely related two orthologs are. our strategy is as follows. first, for a pair of proteins, we determine their orthologs in the model organisms. second, features are extracted for each ortholog pair from gene expression profiles and go annotations of model organisms. finally, the heterogeneous data features are integrated to describe the protein pair of the target organism using a bayesian network-based model that assigns likelihood ratios for interaction. novel feature extraction to determine how likely two proteins will interact, several features are derived from gene expression profiles and go annotations. for each protein pair in the target organism, we identify its ortholog pairs r2) in three model organisms. from each model organism, we download three microarray datasets. for each ortholog pair r2), pearson correlation coefficients are calculated from the gene expression profiles. a 4-level uniform quantization is used and each pcc is discretized into one of four states: high, medium high, medium low, and low. rather than assuming the pccs extracted from different microarray data are independent, we model the three pccs from individual model organism jointly with one node in our bayesian network model. this is very different from the commonly-used naïve bayes method in which every feature is assumed to be independent of each other. moreover, we derive novel features from go annotations for each identified ortholog pair r2) of the protein pair. three unique features are derived from each of the "molecular function", "biological process", and "cellular component" annotations in go. the first feature checks whether two proteins share annotation terms: if the two proteins share at least one common term, the feature value is one; otherwise, it is zero. the second feature is called correlation ratio. in go, gene products can be associated with more than one term. therefore, the correlation between two go terms is defined as the number of gene products in common. the larger the correlation value is, the closer the two go terms are. we examine all possible pairs of go terms between the two proteins and identify two go terms with the largest correlation value. the correlation ratio is then defined as n/, where n is the correlation between term_ and term_ n and n are the numbers of gene products with term_ and term_ respectively. the correlation ratio is also quantized into two levels with a threshold of: high and low. the third feature is based on the minimum go distance d between two proteins. since go is organized as a directed acyclic graph where each node represents a go term, distance between two terms is described as the least number of nodes separating them in the graph. again, to identify the two go terms with the minimum distance, we examine all possible pairs of go terms between the two proteins. for the third feature, incorporating the graph structures, we define eight states: if d is zero; if d is one; if d is two and term_ and term_ have a grandparent-children relationship; if d is two and term_ and term_ are siblings with a common parent term; if d is three and term_ is term_4's great grandparent or vice versa; if d is three and term_3's parent and term_ are siblings with a common parent term; if d is three with all remaining graph structural cases; and if d is larger than three. apparently, the three features are not independent to each other. in our integrative system, we model the three features from each model organism jointly with one node. human protein-protein interaction prediction it is important to investigate how widely applicable our approach is for automatic verification of large sets of interactions. if a method is sufficient, its predicted protein-protein interactions should have higher overlap with the previously established interactions. to evaluate our integrative method, we use both specificity and sensitivity. the specificity is defined as the percentage of matched non-interactions between the predicted set and the observed set over the total number of observed non-interactions. the sensitivity is defined as the percentage of matched interactions over the total number of observed interactions. first of all, with a specificity of 95%, our method can achieve a sensitivity of about 44%, and if the specificity reduces to 50%, the sensitivity increases to 80%. these results clearly demonstrate that model organisms certainly provide significant information for the prediction of ppis in the target organisms. secondly, we compare our method to the commonly-used naïve bayesian method. in the naïve bayesian model, all the features are assumed to be conditionally independent, i.e., features extracted from three microarray data sets and three novel features from go in each model organism are conditionally independent given ppi. table contains results of our method and the naïve bayesian method over the test dataset. with comparable specificities fixed at approximately the same level 70%, our method can achieve 73% in sensitivity and the naïve bayesian can only reach 65% in sensitivity. assessment of yeast protein-protein interaction data while high-throughput technologies generate thousands of protein-protein interactions data and allow for genome-wide analysis, they tend to produce a large number of false positives. on the other hand, low-throughput methods can yield reliable results but are typically labor intensive, time consuming, and on a small scale basis. computational methods provide an ideal tool for evaluating experimentally detected ppis, as in silico methods can utilize existing biological knowledge; predict large-scale ppis; and produce the confidence levels of interactions for each protein pairs. we apply our cross-organism integrative in silico model to evaluate high-throughput yeast ppi data and detect the spurious interactions. our model is ideal for this type of application, as we do not use the direct ppi data from model organisms in our training process. the current available yeast interaction pairs in databases may be determined by various experiments; therefore, the more experiments confirming it, the more confident we are in the interaction. we collected the yeast interaction data from the general repository for interaction datasets. the deposited interactions are determined through a number of methods, but we mainly focus on four: synthetic lethality, affinity capture-ms, two-hybrid, and phenotypic enhancement. total number of ppis detected by each of the experimental methods are and for synthetic lethality, affinity capture-ms, two-hybrid, and phenotypic enhancement, respectively. among the four datasets, total number of unique pairs is therefore, there are overlapping pairs between the datasets. because our goal is to analyze the system on ppis determined by different number of experiments, four data files are generated, in which one contains interaction pairs identified by only one experiment, another contains pairs from only two experiments, etc. finally, there are and ppis identified by one, two, three, and four different experiments, respectively. each ppi pair can be ranked by the likelihood ratio. the larger the ratio is, the higher confidence we have in the interactions. we consider a protein pair as interacting if its likelihood ratio is larger than one. figure shows the percentage of ppis detected by high-throughput methods that are also predicted by our cross-organism model. as can be seen, all the ppis that are supported by four different experiments are also predicted as positives by our model. we also predict 44%, 64%, and 97% of ppis detected by one, two, and three biological experiments as interacting protein pairs, respectively. notably, the percent of true positives for ppis with only one experimental evidence is similar to the positive rate estimated by sprinzak et al.. moreover, we analyze some ppis detected by high-throughput experiments but predicted as negatives by our model. to assess the data, we consider the shortest distance of two proteins in go cellular components, molecular function, and biological process. as discussed by sprinzak et al., for true interactions, the interacting proteins should be localized in the same cellular compartment or participate in the same cellular process. the protein pair, yjl179w and ybr258c, is identified by one high-throughput experiment but predicted as non-interacting by our method. the closest cellular component terms between yjl179w and ybr258c are go: and go:. as can be seen in figure these two proteins are not localized to the same compartment. we can also observe that the two proteins do not participate in the same cellular process nor execute the same function. similar observation can be made regarding protein pairs supported by two biological experiments but predicted as non-interacting. for example, the protein pair, yel061c and ynl147w, has several pairs of go cellular component terms that are closest to each other between the two proteins:,,, and. go: refers to a multisubunit complex that is located at the pericentric region of a condensed chromosome in the nucleus and provides an attachment point for the spindle microtubules. go: describes a semiautonomous, self replicating organelle that occurs in varying numbers, shapes, and sizes in the cytoplasm of virtually all eukaryotic cells. it is notably the site of tissue respiration. go: represents a complex composed of rna of the small nucleolar rna and protein, found in the nucleolus of a eukaryotic cell. go: refers to the ribonucleoprotein complex containing small nuclear rna u6; a component of the major spliceosome complex. go: refers to a complex composed of three small nuclear ribonucleoproteins, snrnp u snrnp u and snrnp u figures and illustrate the closest biological process and molecular function terms, respectively.  the advent of high-throughput technologies has significantly enlarged the collection of protein-protein interactions. on one hand, it has provided a rich source of information for new biological discoveries. on the other hand, it has introduced a technical challenge due to its high error rates. it has been shown by many researchers that the reliability of high-throughput screens is only about 50%. the large number of false positives may result in false biological conclusions. it is thus essential to assess the quality of the interactions. in this paper, we develop a novel bayesian network-based model that integrates heterogeneous data sources from model organisms to determine the probability of two proteins to interact in a target organism. cross-species prediction is attractive as normally we do not have much information about newly sequenced proteins. by mapping them to well studied model organisms; however, we are able to utilize the existing biological knowledge of the model organisms to make accurate predictions. our model is successfully applied to predict protein-protein interactions in human. for the protein pairs with orthologous mappings in all three model organisms, our model can achieve 80% in sensitivity and 70% in specificity. the method is also successfully applied to assess the quality of the high-throughput interaction data. we observed that the more high-throughput experiments confirming an interaction, the higher the confidence score is assigned by our method. for the protein pairs confirmed by four different biological experiments, we predicted all of them as interacting. for the pairs supported by only one experiment, the percentage of true positives we determined is similar to the positive rate estimated by sprinzak et al. the above results demonstrate that model organisms indeed provide important information for protein-protein interaction inference and assessment. the method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. we expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application. 
although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. the proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. in this paper, a novel bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. we observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach. we expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application. we develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. this study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment.
 protein-protein interactions play vital roles in nearly all cellular processes and are involved in the construction of biological pathways such as metabolic and signal transduction pathways. although large-scale experiments have enabled the discovery of thousands of previously unknown linkages among proteins in many organisms, the high-throughput interaction data is often associated with high error rates. since protein interaction networks have been utilized in numerous biological inferences, the inclusive experimental errors inevitably affect the quality of such prediction. thus, it is essential to assess the quality of the protein interaction data.  in this paper, a novel bayesian network-based integrative framework is proposed to assess the reliability of protein-protein interactions. we develop a cross-species in silico model that assigns likelihood scores to individual protein pairs based on the information entirely extracted from model organisms. our proposed approach integrates multiple microarray datasets and novel features derived from gene ontology. furthermore, the confidence scores for cross-species protein mappings are explicitly incorporated into our model. applying our model to predict protein interactions in the human genome, we are able to achieve 80% in sensitivity and 70% in specificity. finally, we assess the overall quality of the experimentally determined yeast protein-protein interaction dataset. we observe that the more high-throughput experiments confirming an interaction, the higher the likelihood score, which confirms the effectiveness of our approach.  this study demonstrates that model organisms certainly provide important information for protein-protein interaction inference and assessment. the proposed method is able to assess not only the overall quality of an interaction dataset, but also the quality of individual protein-protein interactions. we expect the method to continually improve as more high quality interaction data from more model organisms becomes available and is readily scalable to a genome-wide application. 3– november ieee international conference on bioinformatics and biomedicine philadelphia, pa, usa 