BACKGROUND
large-scale protein-protein interactions  have been experimentally identified in several eukaryotic model organisms, such as drosophila melanogaster  <cit> , caenorhabditis elegans  <cit> , and saccharomyces cerevisiae  <cit> .
moreover, thousands of ppis have been collected from web databases including bind  <cit> , cygd  <cit> , dip  <cit> , biogrid  <cit> , intact  <cit> , and mint  <cit> .
although the mammalian interactions, mppi  <cit> , have been published, the amount of the data with similar scale has not been described.
the large-scale set of interactions of human proteins is still hard to determine directly.
many computational methods have been developed to predict protein-protein interactions.
a phylogenetic profile method  <cit>  describes the presence or absence of proteins among different organisms with sequenced genomes.
proteins have similar phylogenetic profiles, between which functional links can be detected.
the gene or domain fusion method  <cit>  describes a pair of proteins encoded as separate genes in one organism and fused into a single protein in another organism.
such a pair of proteins can be inferred by the function link, particularly among metabolic pathways.
in the gene neighbor or gene order method  <cit> , the genes that encode two proteins are adjacent in chromosome proximity in several organisms, and are likely to be functionally linked.
however, this method exploits the prevalance of operons in prokaryotes, but operons appear to be uncommon in eukaryoyes such as humans.
predictions using interologs  <cit>  are based on the theory that proteins interacting in one organism co-evolve such that their respective orthologs maintain the ability to interact in another organism.
the interolog concept has been applied to predict human protein interactions  <cit> .
some bioinformatics models  <cit>  have also been developed to detect interactions among proteins by probability and machine-learning methods and the literature text-mining approach  <cit>  based on natural language processing.
bader et al.
developed a logistic regression approach  <cit>  that adopts employs statistical and topological descriptors to predict the biological relevance of ppis obtained from high-throughput screening for yeast.
other sources of information, such as mrna expression, genetic interactions and database annotations, are subsequently used to validate the model predictions.
lu et al.
used a simple naive bayes classifier to integrate diverse sources of genomic evidence, ranging from co-expression relationships to phylogenetic profiling similarity  <cit> .
the greatest challenge in predicting human ppis using the interolog-based method is that the high-throughput interactions generate too many false positives when applied to phylogenetically distant organisms or lower eukaryotes  <cit> , and some researchers have suggested that only 50% of yeast two-hybrid interactions are reliable  <cit> .
therefore, other filtering examinations of features and scoring schema should be further considered in order to increase the confidence in the prediction of human interactions performed by the interolog-based method.
this study constructs human ppi maps from six eukaryotes, namely rat, mouse, fly, worm, thale cress and baker's yeast.
the quasi-clique is analyzed and determined as a relative conservation score from the protein interaction networks in each organism.
the other feature scores further drawn from spatial proximity , temporal synchronicity  and domain-domain combinations are also inspected, to obtain human ppi networks with confidence scores.
RESULTS
predicted human protein interactions
all protein access codes, such as ncbi gi number or refseq id, were converted into non-redundant uniprot ids.
table  <dig> shows the non-redundant  total set of the originally predicted human protein-protein interactions  derived from six reference organisms.
one-to-many mappings exist across species in the inparanoid-predicted data set, and are applied to identify protein orthologs.
the total data set of  <dig>   <dig> human ppis was obtained by the proposed method without cutoff by confidence score .
a total of  <dig>   <dig> protein interactions were predicted .
the known human interactions  were downloaded from external databases bind, biogrid, dip, hprd, intact, mint and mppi.
the known <dig> data set was derived from known with the addition of two recently published experimental data sets of human ppis  <cit> .
the proposed method predicted all  <dig>   <dig> true positive  and  <dig>   <dig> putatives  interaction data sets when applying the threshold cs ≥  <dig>  a threshold of cs ≥  <dig> achieved  <dig>   <dig> true positives  and  <dig>   <dig> putatives .
figure  <dig> summarizes the results that showing the relationship among these data sets.
the following evaluation compares the functional annotations among the known <dig>  tp <dig>  tp <dig>  pu <dig>  pu <dig>  and random interaction data sets .
evaluation
the experimental human ppis and standard benchmark are limited from well-known databases and few interactions are known completely.
therefore, the absence of interactions between proteins from the experimental databases does not indicate that the interactions are negative.
given this limited knowledge, functional keyword annotation and go term matching were tested to determine the accuracy of measurement of various interaction data sets.
testing for true positives mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaaiabiodaziabigdaxiabieda3aqaaiabisda0iabieda3iabiada2aaacqgh9aqpcqai2agncqai2agncqgguaglcqai2agncqaiwaamcqgglaqjaaa@3920@ and 4741212= <dig> %
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaaiabisda0iabieda3iabisda0aqaaiabigdaxiabikdayiabigdaxiabikdayaaacqgh9aqpcqaizawmcqai5aqocqgguaglcqaixaqmcqaixaqmcqgglaqjaaa@39f8@, respectively .
therefore, human interactions can be confidently predicted from multiple mammalian organisms and higher eukaryotes.
testing scoring method
each feature score of each data set was evaluated to determine whether the proposed scoring method was associated with more accurate predictions of interactions.
the data sets predicted by blast search method  were also compared with our predicted data sets.
in figure  <dig>  each feature score was the original raw score without normalization, revealing that the data sets  predicted by our approach have similar but higher feature scores than those of the known interaction data sets  and the randomly generated data sets .
the distributions of the various components of the confidence metrics and anova tests between these interaction data sets were listed .
the differences between these data sets are statistically significant.
testing functional annotation
interacting proteins commonly have similar functions.
additionally, researchers should be able to validate the functions of predicted protein pairs.
the interactions predicted by the proposed method were optimized in terms of uniprot functional keyword annotations, go 'molecular function'  and go 'biological process' .
their relevant go terms such as 'molecular function unknown', 'obsolete molecular function', 'biological process unknown' and 'obsolete biological process' were discarded.
equations , , and  define the jaccard coefficient of the uniprot keyword, and the deepest depth of common ancestor go terms in mf and bp categories, uk, gmf and gbp, respectively.
uk=kat∗kbkat∗ka+kbt∗kb−kat∗kb
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwgvbqvcqwglbwscqgh9aqpdawcaaqaaiabduealnaadaaaleaacqwghbqyaeaacqwgubavaagccqghxiikcqwglbwsdawgaawcbagaemoyaigabeaaaoqaaiabduealnaadaaaleaacqwghbqyaeaacqwgubavaagccqghxiikcqwglbwsdawgaawcbagaemyyaegabeaakiabgucariabduealnaadaaaleaacqwgibgyaeaacqwgubavaagccqghxiikcqwglbwsdawgaawcbagaemoyaigabeaakiabgkhitiabduealnaadaaaleaacqwghbqyaeaacqwgubavaagccqghxiikcqwglbwsdawgaawcbagaemoyaigabeaaaaaaaa@4f65@
gmf=∑i=1i∗
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=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@7343@
gbp=∑i=1i∗
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=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@733f@
where ka, kb are the keyword vectors of interacting protein pairs a and b, respectively.
for example, in ka =  <cit> , the presence or absence of a keyword are represented as  <dig> or  <dig>  respectively.
protein self-interactions or homo-dimers tend to have high scores, and always share the same functional annotations.
hence, these interactions were eliminated from the predicted pairs to eliminate bias in the results.
first, the number of interaction pairs sharing at least one uniprot overlapping functional keyword was determined to verify the accuracy of the predicted interactions.
second, the number of interaction pairs sharing common go annotations at a particular depth in the go 'molecular function' and 'biological process' hierarchy was analyzed to confirm that the results and that were not just a general go term applied.
comparisons were made among known <dig>  tp <dig>  tp <dig>  btp, pu <dig>  pu <dig>  bpu and randoms data sets .
finally, the probability that two proteins share the same uniprot functional keyword by chance is determined through the hypergeometric distribution  <cit> .
the p-value is obtained by the following equation:
p=∑xn
mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwgwbaccqgh9aqpdaaewbqaamaalaaabawaaewaaeaafaqabegabaaabagaemyta0eabagaemieaghaaagaayjkaiaawmcaamaabmaabaqbaeqabiqaaaqaaiabd6eaojabgkhitiabd2eanbqaaiabd6gaujabgkhitiabdiha4baaaiaawicacaglpaaaaeaadaqadaqaauaabeqaceaaaeaacqwgobgtaeaacqwgubgbaaaacagloagaayzkaaaaaawcbagaemieaghabagaemoba4ganiabgghildaaaa@4533@
where n and m denote the total number of proteins in the population, and the number of proteins that have a particular functional keyword, respectively, and n and x denote the total number of proteins in the set, and the number of proteins annotated with the particular functional keyword, respectively.
since a pair of proteins is observed, both n and x are equal to  <dig>  a protein pair is treated as enriched by a uniprot functional if the corrected p-value is ≤  <dig> .
the total of  <dig>   <dig> predicted interactions with this p-value are listed .
testing conservation score  and interolog score
comparisons
comparison with cut-off scores
* : the predicted data set do not contain negative data to calculate false negative  to obtain recall = )
the cut-off threshold of confidence score , equation , was identified to increase the true positive ratio and indicate the relationship between the number of predicted interactions and the coverage of known interactions.
the maximum precision was obtained by a threshold of cs ≥  <dig>  table  <dig> shows the relationship between cut-off threshold and predicted data sets from the  <dig>   <dig> known interactions in the six reference organisms.
* : the predicted data set do not contain negative data to calculate false negative  to obtain recall = )
comparison with blast data sets
all of the  <dig>   <dig> known interactions  from the six reference organisms were used in the orthology search by blast with minimum e-value .
the protein sequences were downloaded from uniprot.
the inparanoid one-to-one mapping  and one-to-many mappings  were also compared, as were the inparanoid data sets with threshold cs =  <dig>  table  <dig> shows the results of these predictions.
although blast can more true positive interologs in quantity than the inparanoid method, it also produced a higher putative ratio.
the predicted and true positive ratios reveal that inparanoid can distinguish potential true orthologs.
the btp and bpu are data sets for true positive and putative predicted from blast mapping method, respectively.
the scoring method testing results are also presented .
comparison with experimental data sets
all of the proteins were mapped to uniprot entry id, and proteins  that could not be confidently mapped were eliminated.
figure  <dig> presents the overlap among various interacting data sets, including two human experimental networks  <cit>  and our predicted interlogs from six reference organisms .
surprisingly, the results of the proposed interolog-based approach and the experimental high-throughput method did not overlap significantly, revealing that the methods applied to detect interactions have different biases.
therefore, two methods  may reveal different and partial sub-networks of the whole human protein interaction network.
the proposed method is based on evolutionarily conserved interologs, and can not distinguish between species-specific interactions from the two experimental data sets.
comparison with interolog-based approach
the proposed method was compared with other interolog-based methods for predicting human ppis, namely homomint  <cit> , hpid  <cit> , ippred  <cit> , the method of lehner et al.
's group  <cit> , ophid  <cit> , point  <cit>  and rhodes et al.
's method  <cit> .
the properties of the ortholog identification methods and other features are as follows.
• an ortholog identification method indicates the orthologs between model organisms.
orthologs between organisms do not have a one-to-one relationship with blast search  or blast search with e-value ; yet one-to-many and many-to-many mappings exist.
the inparanoid clustering algorithm distinguishes potential true orthologs from paralogs according to the inparanoid score .
although similar structures typically share similar biological functions, the structural classification at the protein superfamily level  is not trivial in the identification of structural similarities at the human protein level on the large scale.
• other features indicate that some other factors affecting their interactions are considered.
the quasi-clique with maximal conservation score , domain-domain combinations , sub-cellular localization , cell-cycle phase  and tissue-specificity  were also carefully examined in this study.
other existing methods apply the 'biological process'  and 'molecular function'  annotations in the go hierarchy.
the brief comparisons in table  <dig> reveal that the proposed method predicts results based on the relative conservation score and the other feature scores to obtain human ppi networks through confidence scores.
a confidence score allows researchers to identify interactions qualitatively from objective and biologically reasonable judgement, rather than using a large quantity of interacting data without prioritized selection.
biological significance
many predicted pairs have been identified in existing known human ppi databases  and the two human experimental ppis data sets .
the top  <dig> predictions that were not identified or not present in the existing databases were listed  using the proposed prediction system, and indicate that some top predicted protein interacting pairs were manifestations of their potentially physical interactions.
for example, for the top  <dig> plk <dig> and stk <dig> interaction, plk <dig>  has just been reported this year that it interacts with aurora-b in playing critical roles in the regulation of chromosomal dynamics  <cit> .
stk <dig> is also known as aurora-a.
the kinase domains of aurora-a and aurora-b share more than 70% of their sequence data.
most importantly, in 3d structure, they are likely to share partially similar surface features  <cit> .
therefore, the interaction of aurora-a  with plk <dig>  is not surprising.
orc <dig>  the origin recognition complex protein, binds specifically to origins of replication, and serves as a platform for the assembly of additional initial factors including mcm and cdc <dig> proteins.
mcm proteins form a hexameric structure complex with  <dig> subunits, namely mcm <dig>  mcm <dig>  mcm <dig>  mcm <dig>  mcm <dig> and mcm <dig>  <cit> .
to date, orc <dig> been confirmed to interact with mcm <dig> and mcm <dig>  orc <dig> can also be reasonably expected to interact with mcm <dig>  and mcm <dig> , because they are all localized in a complex or origin recognition site.
furthermore, since mcm proteins form a hexamer, mcm <dig> can reasonably be expected to interact with mcm <dig> , and mcm <dig> can be expected to interact with mcm <dig> .
these findings reveal that constructing a protein-protein interaction network allows novel interacting proteins to be identified.
all proteins of the prediction pairs are linked to a human disease in the omim database  <cit>  whenever possible .
therefore, the interaction network can be further extended through these annotated disease-associated proteins.
moreover, these predicted interactions have high conservation  and interolog  scores , revealing that these interactions are evolutionarily conserved across species.
discussion
important high-throughput approaches such as yeast two-hybrid have recently been applied to systematically identify ppis in humans .
surprisingly, the experimental results of the proposed and high-throughput methods did not overlap significantly, indicating that different biases exist because of the approaches applied to detect interactions.
hence, two methods  may indicate different and partial sub networks of the complete human-protein interaction network.
the accuracy of the predicted interactions depends mainly on the quality and completeness of the reference model organism interaction data sets.
although only a subset of the known interactions in the human interaction network can currently be accurately predicted , the accuracy can be improved by large-scale protein interaction data in 'higher' eukaryotic reference model organisms in the future.
the orthologous relationship between sequence and function is difficult to evaluate, because no clear measurement of functional similarity between any pair of proteins is made.
many one-to-many and many-to-many mappings exist across species, and can be used to identify protein orthologs.
the inparanoid algorithm was applied because several proteins from so-called 'lower' eukaryotes have many co-orthologs in humans, and can be identified using inparanoid, but not with a simple one-to-one sequence similarity search based on blast or structural classification at the protein superfamily level.
the interolog  <cit>  concept was previously proposed to predict c. elegans ppis from yeast.
this study presents 'interolog' as a concrete method for predicting human ppis from those of six 'lower' eukaryotes.
however, high-throughput interactions with false positives and false negatives have been noted in some eukaryotes  <cit> .
this study utilized other features and scoring schema to derive the confidence with which human interactions are predicted using the interolog-based method.
computational analysis can be applied to determine conservation scores and other feature scores, and is readily extensible to any newly sequenced genomes.
users can construct many genome-wide ppi networks with high confidence using interolog mapping and the proposed scoring method.
this concept can also be applied to discover transcription networks, such as simultaneous protein-dna and protein-protein interaction networks  <cit> .
CONCLUSIONS
the evolution of ppis from the relative conservation score is comprehensively assessed by finding a quasi-clique from protein networks.
however, ppis in biological organisms are complex, and do not depend only on a single feature, such as protein structural complementarity, gene proximity or co-evolution.
moreover, some other protein interaction features, including sub-cellular localization, tissue specificity, cell-cycle stage and domain-domain combinations, are also critical factors to be considered.
this study describes a scoring method based on integrating these heterogeneous but significant biological resources to prioritize human protein-protein interacting networks.
the analytical results indicate that the proposed method can predict potential human ppis with higher confidence than the other methods studied .
the analytical results also reveal that some correlations exist between the true positive data set and the data set produced by the proposed method .
furthermore, the conservation score of a true positive interaction data set is higher that the score of the putative interaction data set .
additionally, the proposed method allows researchers to identify quantitatively, rather than simply qualitatively, how , when  and where  the two proteins interact, using a confidence score.

 large-scale protein-protein interactions have been experimentally identified in several eukaryotic model organisms, such as drosophila melanogaster, caenorhabditis elegans, and saccharomyces cerevisiae. moreover, thousands of ppis have been collected from web databases including bind, cygd, dip, biogrid, intact, and mint. although the mammalian interactions, mppi, have been published, the amount of the data with similar scale has not been described. the large-scale set of interactions of human proteins is still hard to determine directly. many computational methods have been developed to predict protein-protein interactions. a phylogenetic profile method describes the presence or absence of proteins among different organisms with sequenced genomes. proteins have similar phylogenetic profiles, between which functional links can be detected. the gene or domain fusion method describes a pair of proteins encoded as separate genes in one organism and fused into a single protein in another organism. such a pair of proteins can be inferred by the function link, particularly among metabolic pathways. in the gene neighbor or gene order method, the genes that encode two proteins are adjacent in chromosome proximity in several organisms, and are likely to be functionally linked. however, this method exploits the prevalance of operons in prokaryotes, but operons appear to be uncommon in eukaryoyes such as humans. predictions using interologs are based on the theory that proteins interacting in one organism co-evolve such that their respective orthologs maintain the ability to interact in another organism. the interolog concept has been applied to predict human protein interactions. some bioinformatics models have also been developed to detect interactions among proteins by probability and machine-learning methods and the literature text-mining approach based on natural language processing. bader et al. developed a logistic regression approach that adopts employs statistical and topological descriptors to predict the biological relevance of ppis obtained from high-throughput screening for yeast. other sources of information, such as mrna expression, genetic interactions and database annotations, are subsequently used to validate the model predictions. lu et al. used a simple naive bayes classifier to integrate diverse sources of genomic evidence, ranging from co-expression relationships to phylogenetic profiling similarity. the greatest challenge in predicting human ppis using the interolog-based method is that the high-throughput interactions generate too many false positives when applied to phylogenetically distant organisms or lower eukaryotes, and some researchers have suggested that only 50% of yeast two-hybrid interactions are reliable. therefore, other filtering examinations of features and scoring schema should be further considered in order to increase the confidence in the prediction of human interactions performed by the interolog-based method. this study constructs human ppi maps from six eukaryotes, namely rat, mouse, fly, worm, thale cress and baker's yeast. the quasi-clique is analyzed and determined as a relative conservation score from the protein interaction networks in each organism. the other feature scores further drawn from spatial proximity, temporal synchronicity and domain-domain combinations are also inspected, to obtain human ppi networks with confidence scores.  predicted human protein interactions all protein access codes, such as ncbi gi number or refseq id, were converted into non-redundant uniprot ids. table shows the non-redundant total set of the originally predicted human protein-protein interactions derived from six reference organisms. one-to-many mappings exist across species in the inparanoid-predicted data set, and are applied to identify protein orthologs. the total data set of human ppis was obtained by the proposed method without cutoff by confidence score. a total of protein interactions were predicted. the known human interactions were downloaded from external databases bind, biogrid, dip, hprd, intact, mint and mppi. the known data set was derived from known with the addition of two recently published experimental data sets of human ppis. the proposed method predicted all true positive and putatives interaction data sets when applying the threshold cs ≥ a threshold of cs ≥ achieved true positives and putatives. figure summarizes the results that showing the relationship among these data sets. the following evaluation compares the functional annotations among the known tp tp pu pu and random interaction data sets. evaluation the experimental human ppis and standard benchmark are limited from well-known databases and few interactions are known completely. therefore, the absence of interactions between proteins from the experimental databases does not indicate that the interactions are negative. given this limited knowledge, functional keyword annotation and go term matching were tested to determine the accuracy of measurement of various interaction data sets. testing for true positives mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaaiabiodaziabigdaxiabieda3aqaaiabisda0iabieda3iabiada2aaacqgh9aqpcqai2agncqai2agncqgguaglcqai2agncqaiwaamcqgglaqjaaa@3920@ and 4741212= % mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaadawcaaqaaiabisda0iabieda3iabisda0aqaaiabigdaxiabikdayiabigdaxiabikdayaaacqgh9aqpcqaizawmcqai5aqocqgguaglcqaixaqmcqaixaqmcqgglaqjaaa@39f8@, respectively. therefore, human interactions can be confidently predicted from multiple mammalian organisms and higher eukaryotes. testing scoring method each feature score of each data set was evaluated to determine whether the proposed scoring method was associated with more accurate predictions of interactions. the data sets predicted by blast search method were also compared with our predicted data sets. in figure each feature score was the original raw score without normalization, revealing that the data sets predicted by our approach have similar but higher feature scores than those of the known interaction data sets and the randomly generated data sets. the distributions of the various components of the confidence metrics and anova tests between these interaction data sets were listed. the differences between these data sets are statistically significant. testing functional annotation interacting proteins commonly have similar functions. additionally, researchers should be able to validate the functions of predicted protein pairs. the interactions predicted by the proposed method were optimized in terms of uniprot functional keyword annotations, go'molecular function' and go'biological process'. their relevant go terms such as'molecular function unknown','obsolete molecular function','biological process unknown' and'obsolete biological process' were discarded. equations,, and define the jaccard coefficient of the uniprot keyword, and the deepest depth of common ancestor go terms in mf and bp categories, uk, gmf and gbp, respectively. uk=kat∗kbkat∗ka+kbt∗kb−kat∗kb mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwgvbqvcqwglbwscqgh9aqpdawcaaqaaiabduealnaadaaaleaacqwghbqyaeaacqwgubavaagccqghxiikcqwglbwsdawgaawcbagaemoyaigabeaaaoqaaiabduealnaadaaaleaacqwghbqyaeaacqwgubavaagccqghxiikcqwglbwsdawgaawcbagaemyyaegabeaakiabgucariabduealnaadaaaleaacqwgibgyaeaacqwgubavaagccqghxiikcqwglbwsdawgaawcbagaemoyaigabeaakiabgkhitiabduealnaadaaaleaacqwghbqyaeaacqwgubavaagccqghxiikcqwglbwsdawgaawcbagaemoyaigabeaaaaaaaa@4f65@ gmf=∑i=1i∗ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=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@7343@ gbp=∑i=1i∗ mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=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@733f@ where ka, kb are the keyword vectors of interacting protein pairs a and b, respectively. for example, in ka =, the presence or absence of a keyword are represented as or respectively. protein self-interactions or homo-dimers tend to have high scores, and always share the same functional annotations. hence, these interactions were eliminated from the predicted pairs to eliminate bias in the results. first, the number of interaction pairs sharing at least one uniprot overlapping functional keyword was determined to verify the accuracy of the predicted interactions. second, the number of interaction pairs sharing common go annotations at a particular depth in the go'molecular function' and'biological process' hierarchy was analyzed to confirm that the results and that were not just a general go term applied. comparisons were made among known tp tp btp, pu pu bpu and randoms data sets. finally, the probability that two proteins share the same uniprot functional keyword by chance is determined through the hypergeometric distribution. the p-value is obtained by the following equation: p=∑xn mathtype@mtef@5@5@+=feaafiart1ev1aaatcvaufkttlearuwrp9mdh5mbpbiqv92aaexatlxbi9gbaebbnrfifhhdyfgasaach8aky=wiffydh8gipec8eeeu0xxdbba9frfj0=oqffea0dxdd9vqai=hguq8kuc9pgc9s8qqaq=dirpe0xb9q8qilsfr0=vr0=vr0dc8meaabaqaciaacagaaeqabaqabegadaaakeaacqwgwbaccqgh9aqpdaaewbqaamaalaaabawaaewaaeaafaqabegabaaabagaemyta0eabagaemieaghaaagaayjkaiaawmcaamaabmaabaqbaeqabiqaaaqaaiabd6eaojabgkhitiabd2eanbqaaiabd6gaujabgkhitiabdiha4baaaiaawicacaglpaaaaeaadaqadaqaauaabeqaceaaaeaacqwgobgtaeaacqwgubgbaaaacagloagaayzkaaaaaawcbagaemieaghabagaemoba4ganiabgghildaaaa@4533@ where n and m denote the total number of proteins in the population, and the number of proteins that have a particular functional keyword, respectively, and n and x denote the total number of proteins in the set, and the number of proteins annotated with the particular functional keyword, respectively. since a pair of proteins is observed, both n and x are equal to a protein pair is treated as enriched by a uniprot functional if the corrected p-value is ≤. the total of predicted interactions with this p-value are listed. testing conservation score and interolog score comparisons comparison with cut-off scores *: the predicted data set do not contain negative data to calculate false negative to obtain recall = ) the cut-off threshold of confidence score, equation, was identified to increase the true positive ratio and indicate the relationship between the number of predicted interactions and the coverage of known interactions. the maximum precision was obtained by a threshold of cs ≥ table shows the relationship between cut-off threshold and predicted data sets from the known interactions in the six reference organisms. *: the predicted data set do not contain negative data to calculate false negative to obtain recall = ) comparison with blast data sets all of the known interactions from the six reference organisms were used in the orthology search by blast with minimum e-value. the protein sequences were downloaded from uniprot. the inparanoid one-to-one mapping and one-to-many mappings were also compared, as were the inparanoid data sets with threshold cs = table shows the results of these predictions. although blast can more true positive interologs in quantity than the inparanoid method, it also produced a higher putative ratio. the predicted and true positive ratios reveal that inparanoid can distinguish potential true orthologs. the btp and bpu are data sets for true positive and putative predicted from blast mapping method, respectively. the scoring method testing results are also presented. comparison with experimental data sets all of the proteins were mapped to uniprot entry id, and proteins that could not be confidently mapped were eliminated. figure presents the overlap among various interacting data sets, including two human experimental networks and our predicted interlogs from six reference organisms. surprisingly, the results of the proposed interolog-based approach and the experimental high-throughput method did not overlap significantly, revealing that the methods applied to detect interactions have different biases. therefore, two methods may reveal different and partial sub-networks of the whole human protein interaction network. the proposed method is based on evolutionarily conserved interologs, and can not distinguish between species-specific interactions from the two experimental data sets. comparison with interolog-based approach the proposed method was compared with other interolog-based methods for predicting human ppis, namely homomint, hpid, ippred, the method of lehner et al.'s group, ophid, point and rhodes et al.'s method. the properties of the ortholog identification methods and other features are as follows. • an ortholog identification method indicates the orthologs between model organisms. orthologs between organisms do not have a one-to-one relationship with blast search or blast search with e-value; yet one-to-many and many-to-many mappings exist. the inparanoid clustering algorithm distinguishes potential true orthologs from paralogs according to the inparanoid score. although similar structures typically share similar biological functions, the structural classification at the protein superfamily level is not trivial in the identification of structural similarities at the human protein level on the large scale. • other features indicate that some other factors affecting their interactions are considered. the quasi-clique with maximal conservation score, domain-domain combinations, sub-cellular localization, cell-cycle phase and tissue-specificity were also carefully examined in this study. other existing methods apply the'biological process' and'molecular function' annotations in the go hierarchy. the brief comparisons in table reveal that the proposed method predicts results based on the relative conservation score and the other feature scores to obtain human ppi networks through confidence scores. a confidence score allows researchers to identify interactions qualitatively from objective and biologically reasonable judgement, rather than using a large quantity of interacting data without prioritized selection. biological significance many predicted pairs have been identified in existing known human ppi databases and the two human experimental ppis data sets. the top predictions that were not identified or not present in the existing databases were listed using the proposed prediction system, and indicate that some top predicted protein interacting pairs were manifestations of their potentially physical interactions. for example, for the top plk and stk interaction, plk has just been reported this year that it interacts with aurora-b in playing critical roles in the regulation of chromosomal dynamics. stk is also known as aurora-a. the kinase domains of aurora-a and aurora-b share more than 70% of their sequence data. most importantly, in 3d structure, they are likely to share partially similar surface features. therefore, the interaction of aurora-a with plk is not surprising. orc the origin recognition complex protein, binds specifically to origins of replication, and serves as a platform for the assembly of additional initial factors including mcm and cdc proteins. mcm proteins form a hexameric structure complex with subunits, namely mcm mcm mcm mcm mcm and mcm. to date, orc been confirmed to interact with mcm and mcm orc can also be reasonably expected to interact with mcm and mcm, because they are all localized in a complex or origin recognition site. furthermore, since mcm proteins form a hexamer, mcm can reasonably be expected to interact with mcm, and mcm can be expected to interact with mcm. these findings reveal that constructing a protein-protein interaction network allows novel interacting proteins to be identified. all proteins of the prediction pairs are linked to a human disease in the omim database whenever possible. therefore, the interaction network can be further extended through these annotated disease-associated proteins. moreover, these predicted interactions have high conservation and interolog scores, revealing that these interactions are evolutionarily conserved across species. discussion important high-throughput approaches such as yeast two-hybrid have recently been applied to systematically identify ppis in humans. surprisingly, the experimental results of the proposed and high-throughput methods did not overlap significantly, indicating that different biases exist because of the approaches applied to detect interactions. hence, two methods may indicate different and partial sub networks of the complete human-protein interaction network. the accuracy of the predicted interactions depends mainly on the quality and completeness of the reference model organism interaction data sets. although only a subset of the known interactions in the human interaction network can currently be accurately predicted, the accuracy can be improved by large-scale protein interaction data in'higher' eukaryotic reference model organisms in the future. the orthologous relationship between sequence and function is difficult to evaluate, because no clear measurement of functional similarity between any pair of proteins is made. many one-to-many and many-to-many mappings exist across species, and can be used to identify protein orthologs. the inparanoid algorithm was applied because several proteins from so-called'lower' eukaryotes have many co-orthologs in humans, and can be identified using inparanoid, but not with a simple one-to-one sequence similarity search based on blast or structural classification at the protein superfamily level. the interolog concept was previously proposed to predict c. elegans ppis from yeast. this study presents'interolog' as a concrete method for predicting human ppis from those of six'lower' eukaryotes. however, high-throughput interactions with false positives and false negatives have been noted in some eukaryotes. this study utilized other features and scoring schema to derive the confidence with which human interactions are predicted using the interolog-based method. computational analysis can be applied to determine conservation scores and other feature scores, and is readily extensible to any newly sequenced genomes. users can construct many genome-wide ppi networks with high confidence using interolog mapping and the proposed scoring method. this concept can also be applied to discover transcription networks, such as simultaneous protein-dna and protein-protein interaction networks.  the evolution of ppis from the relative conservation score is comprehensively assessed by finding a quasi-clique from protein networks. however, ppis in biological organisms are complex, and do not depend only on a single feature, such as protein structural complementarity, gene proximity or co-evolution. moreover, some other protein interaction features, including sub-cellular localization, tissue specificity, cell-cycle stage and domain-domain combinations, are also critical factors to be considered. this study describes a scoring method based on integrating these heterogeneous but significant biological resources to prioritize human protein-protein interacting networks. the analytical results indicate that the proposed method can predict potential human ppis with higher confidence than the other methods studied. the analytical results also reveal that some correlations exist between the true positive data set and the data set produced by the proposed method. furthermore, the conservation score of a true positive interaction data set is higher that the score of the putative interaction data set. additionally, the proposed method allows researchers to identify quantitatively, rather than simply qualitatively, how, when and where the two proteins interact, using a confidence score. 
the predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast. the recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. specifically, the availability of information about experimental protein-protein interactions and other protein features on the internet enables human protein-protein interactions to be computationally predicted from co-evolution events. this study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction. this study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods. evaluation results of the proposed method using functional keyword and gene ontology annotations indicate that some confidence is justified in the accuracy of the predicted interactions. the scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs.
 the recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. specifically, the availability of information about experimental protein-protein interactions and other protein features on the internet enables human protein-protein interactions to be computationally predicted from co-evolution events. this study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction.  this study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. the scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. the predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast.  evaluation results of the proposed method using functional keyword and gene ontology annotations indicate that some confidence is justified in the accuracy of the predicted interactions. comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods. 