BACKGROUND
microarrays are routinely used to simultaneously assess the relative expression levels of many thousands of gene transcripts in biological samples.
increasingly sophisticated techniques have been developed to detect those transcripts among thousands whose expression levels have significantly changed in response to the selected experimental conditions.
yet these lists of differentially expressed genes, however highly refined, are of limited use in themselves.
a deeper understanding of the underlying biological mechanisms depends on the identification of how these genes are interacting with the cell.
through the development of gene interaction networks, microarray data evolve from being descriptive to being predictive.
the use of various statistical methods for identifying significant transcriptional regulatory interactions and for uncovering the corresponding regulatory network structures is a major step toward that direction  <cit> .
however, microarray data themselves do not provide enough information for the definitive identification of regulatory interactions within the cell.
the inclusion of other biological information is essential.
recently, several groups have proposed methods of constructing regulatory networks based on microarray gene expressions and other large sets of high throughput data  <cit> .
particularly, the methods in  <cit>  can perform analysis of a highly diverse collection of genome-wide data sets, which include gene expression, protein interactions, growth phenotype data, and transcription factor binding, to reveal the modular organization of the yeast system.
the identification of regulatory networks at genome scale is important.
however, it does not address the issue of analyzing a single set of microarray data obtained from a specific hypothesis-driven experiment.
it is possible that the network structure derived based on the above method may be not all observed in a single microarray experiment that was conducted under specific experimental conditions.
therefore, it remains an important task for individual investigators to identify the interactions that are most consistent with the data.
there are several databases to which a set of genes can be submitted for querying of possible pathways.
however, the discovery of new pathways is often not possible by using these databases.
this situation has become a major obstacle for deriving new testable hypotheses from a single set of microarray data conducted by an individual research group.
on the other hand, the large body of information hidden in the published literature has not been effectively integrated in the microarray data analysis.
several groups  <cit>  have attempted to utilize prior biological knowledge to substantially reduce false positives in the outcomes from the data-driven approaches.
herrgard et al .
<cit>  have evaluated networks reconciled from gene expression data, known genome-scale regulatory network structures generated from annotated genome information, well-curated databases, and primary research literature.
jenssen et al.
<cit>  proposed a gene-to-gene co-citation network for  <dig>  named human genes by an automated analysis of titles and abstract in over  <dig> million medline records.
pathwayassist  <dig>  analysis software by ariadne genomics  <cit>  also provides networks for a set of query genes based on a natural language search algorithm of all available pubmed published abstracts.
the limitation of these methods is their inability of discovering novel interactions, since the reported interactions in literature are often used to overlap with the interactions identified from the analysis of microarray data.
ideally, a method for determining gene interaction networks would have the ability to  use the large amount of prior biological knowledge available to researchers and  identify novel gene interactions in a set of microarray expression data for a specific study.
the method proposed here attempts to address these two requirements.
our method is designed to identify small number of testable hypotheses from a specific microarray data set and collected biological observations.
in order to evaluate our method, we define the type of gene interaction considered in this paper as follows.
as microarray experiments measure changes in gene expression, the gene interaction used here will be restricted to what might be reasonably expected to be observed in a microarray experiment: a change in the expression of a regulator gene modulates the expression of a target gene.
these pairwise interactions form the gene interaction network of a particular system under a particular condition.
RESULTS
datasets
the datasets used in this study are subsets of cell-cycle dependent genes in the budding yeast saccharomyces cerevisiae microarray experiments  <cit> .
these microarray experiments were designed to create a comprehensive list of yeast genes whose transcription levels were expressed periodically within the cell cycle.
microarrays were prepared for this study by printing pcr-generated probes.
in order to study cell cycle related gene expression, the cell cycles of yeast cultures needed to be synchronized.
a sample of growth media would contain cells all in the same stage of the cell cycle.
the study used for synchronization of the cell cycle is alpha-factor based.
alpha-factor, a signaling pheromone, causes cells to undergo cell cycle arrest.
pelleting the cells and re-suspending them in fresh media permits normal cell cycle to continue.
for the alpha-factor experiment, the gene expressions of cell cycle synchronized yeast cultures were collected over  <dig> time points taken in 7-minute intervals.
this time series covers more than two complete cycles of cell division.
two subsets of the data were selected for our study.
the first is a subset of  <dig> genes that includes  <dig> known transcription regulators and their possible regulatory targets  <cit> .
this set is highly enriched for known interacting genes involved in the saccharomyces cell cycle.
the second subset is comprised of  <dig> of the most cyclically regulated genes in the microarray experiments.
in the analysis of a hypothesis-driven experiment, it is neither necessary nor logical to investigate every gene in the genome of an organism.
it can be expected that the majority of genes in the genome are irrelevant to the biological phenomenon under investigation.
it might be reasonable, for example, to restrict analysis to those genes observed to undergo statistically significant changes or genes that are detected as being significantly expressed.
as the data were collected in an experiment to observe genes that are instrumental to directing cell cycle activities, only those genes identified as being cyclically expressed will be used in our analysis.
though there is a number of methods that have been used in the past for finding cyclic expression patterns  <cit> , the method used here most resembles that of filkov et al .
<cit> .
in this, stretches of gene expression data are compared against themselves to look for recurring patterns of expression.
this was carried out through the computation of the cyclic correlation coefficient  of a gene.
see methods section for the definition.
the  <dig> genes with the highest ccc were selected for analysis.
the expressions of the genes were identified to fit well with their expected phase in the mitotic cycle.
the annotations of these genes were identified to be enriched for annotations specific to cell cycle.
identify gene interaction pairs from the likelihood of interaction scores and the modified pearson correlation coefficients
one of the goals of this study is to identify gene pairs that are likely to interact on the basis of prior biological knowledge.
the likelihood of interaction  score is the result of efforts to achieve this goal.
using prior information in the pubmed database of scientific publications, information about previously observed gene interactions was collected and used to generate an loi-score for a gene pair.
this score was used to determine if a gene pair is likely a potential interaction pair or not .
if the gene pair closely resembles gene interaction pairs frequently observed in the literature, it is considered likely and should have a high loi-score; if the gene pair has little similarity to previously observed gene interactions, their interaction is considered unlikely and should have a negative loi-score.
loi-scores for all possible pairs of genes from a set of query genes obtained from an experiment will be assigned and arranged in an loi-score matrix.
an excel template of the program for calculating loi-scores was made available  <cit> .
on the other hand, a modified pearson correlation coefficient  was used to identify the possible gene interaction pairs from the microarray data.
a regulation interaction defined in this genetic network is a gene whose change in expression levels has an effect on the subsequent expression of another gene.
the 1-time point shift of the putative target gene relative to the regulator gene used here identifies this type of causal relationships in a simplified way.
two sets of possible gene interactions are considered significant after the multiple hypothesis testing: one obtained from loi-scores and the other one obtained from pccs.
the former set is determined by our prior biological knowledge on a given set of genes collected from scientific literature.
the latter set includes statistically significant interactions identified from the analysis of the microarray data from a specific hypothesis-driven experiment.
the overlap of these two sets forms a network of gene interactions.
these interactions in the network were analyzed to determine how many of the previously published interactions were identified, as well as the number of gene interaction pairs in which both the regulator and the target sharing at least one biological process  annotation as provided by the gene ontology  slim mapper  <cit>  in saccharomyces genome database.
good gene interaction networks are expected to be enriched for both previously published gene interactions and interaction pairs that share go bp annotations.
results of using different thresholds q  for false positive rate   <cit>  for each set, denoted by loi.fdr and pcc.fdr for loi-scores and pccs respectively, and their common sets with different thresholds are presented below.
102-gene set
the results for the 102-gene cell cycle set are summarized in table  <dig>  at pcc.fdr,  <dig> regulatory interaction pairs are identified,  <dig>  of which had been previously reported in an automated survey of the scientific literature, and  <dig>  interaction pairs share at least one go bp annotation between the regulator and the target.
at loi.fdr,  <dig>  regulatory interaction pairs are identified,  <dig>  of which have been previously reported, and  <dig>  of the identified interaction pairs share a go bp annotation between the regulator and target.
combining loi.fdr and pcc.fdr, the method generates a network of  <dig> interaction pairs,  <dig>  of which have been previously reported and  <dig>  of the identified interaction pairs share a go bp annotation between the regulator and target.
a: go-simple: a pair of genes is considered potentially interacting if one of their go annotation pairs has been reported.
b: set of interactions obtained by applying only pcc thresholds;
c: set of interactions obtained by applying only loi thresholds;
d: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr;
e: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr;
f: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr.
999-gene set
results for the set of  <dig> cyclically expressed genes are summarized in table  <dig>  at pcc.fdr,  <dig>  regulatory interaction pairs are identified,  <dig>  of which had been previously reported in an automated survey of the scientific literature, and  <dig>  interaction pairs share at least one go bp annotation between the regulator and the target.
at loi.fdr,  <dig>  interaction pairs are identified,  <dig>  of which have been previously reported, and  <dig>   of the identified interaction pairs share a go bp annotation between the regulator and target.
the set of interaction pairs with an loi.fdr less than  <dig>  shows slight improvement over that of loi.fdr.
combining loi.fdr and pcc.fdr in the 999-gene set, the method generates a network of  <dig> interaction pairs,  <dig>  of which have been previously reported and  <dig>  of the identified interaction pairs share a go bp annotation between the regulator and target, a significant increase.
a: go-simple: a pair of genes is considered potentially interacting if one of their go annotation pairs has been reported.
b: set of interactions obtained by applying only pcc thresholds;
c: set of interactions obtained by applying only loi thresholds;
d: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr;
e: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr;
f: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr.
from tables  <dig> and  <dig>  it can be seen that the total number of previously published interactions remains almost entirely the same at different pcc.fdr when the loi.fdr stringency is fixed at any level.
increasing loi.fdr has the effect of reducing the total number of predicted interactions without reducing the total number of previously published interactions.
in every case, fixing the loosely restrictive pcc.fdr, the application of increasingly stringent loi.fdr improves the generated interaction network with respect to the percent of identified edges that have been previously published.
this can be explained as follows.
the loi-scores for most of the published interactions in both sets are associated with substantially lower p-values in comparison to those unpublished pairs.
more stringent loi.fdr can eliminate those unpublished pairs with relatively higher p-values for loi-scores without excluding published pairs.
the remaining unpublished pairs are likely to have loi-scores with statistically significance and therefore are likely novel interacting pairs.
at the first impression, it seems that the 102-gene set performed better than the 999-gene set.
there are, however, proportionately far fewer published interactions in the 999-gene set  than in the 102-gene set .
for the 102-gene set, interaction set determined the thresholds loi.fdr and pcc.fdr has  <dig> % published interactions, which is about a 4-fold increase over the  <dig> % published interactions out of all possible interactions.
for the 999-gene set, the interaction set determined by the thresholds loi.fdr and pcc.fdr has  <dig> % published interactions, which is about a 22-fold increase over the  <dig> % published interactions out of all possible interactions.
therefore, the result for the 999-gene set is actually a considerable improvement over the result for the 102-gene set.
the method does a better job of finding published interactions in a search space where published interactions are proportionately much sparser.
similarly, the distributions of go bp annotations in the 102-gene and 999-gene sets have a significant effect on the generated interaction networks.
the 102-gene set was selected to include known cell cycle regulated genes and thus tend to be enriched for a more limited selection of largely cell cycle related go bp annotations.
<dig> % of all possible gene interaction pairs in the 102-gene set share a go bp annotation.
the 999-gene set, on the other hand, was selected solely on the observed experimental data.
of all possible interaction pairs in the 999-gene set, there are  <dig> % interaction pairs that share a go bp annotation.
a breakdown of the numbers of shared go bp annotations for the identified interactions in the 999-gene set is shown in table  <dig>  though the 102-gene set appears to perform better, identifying  <dig> %  interaction pairs that share a go bp annotation compared to the 999-gene set with  <dig> %  at loi.fdr and pcc.fdr .
again, the method is actually doing a better job on the larger 999-gene set for finding matching go bp annotation interaction pairs from a less enriched background.
discussion
possible novel interactions in the identified subnetworks
in the network identified at loi.fdr and pcc.fdr for the 999-gene set,  <dig> previously published gene interactions were identified.
the majority of these are interactions of transcriptional regulation, specifically the correct identification of transcription factors swi <dig>  fkh <dig>  and fhl <dig> and several of their regulatory targets  <cit> .
additionally however, regulatory interactions other than simple transcription factors were found, such as kinase regulatory interaction mechanisms like gin <dig> and kcc <dig> indirectly regulating expression swe <dig>  <cit> .
the correct identification of these known interactions adds credence to the possibility that some identified interactions which are not previously reported may be novel discoveries.
these potential discoveries should be considered testable hypotheses for future experimentation.
there are a number of such potentially novel discoveries in the results from the interaction network of the 999-gene set.
some potential transcription factor regulatory interactions, specifically  fhl <dig> regulating ded <dig> and pfk <dig>   stb <dig> regulating cdc <dig>  and  swi <dig> regulating cdc <dig> all have regulation targets near appropriate transcription factor binding sites, though these interactions have not previously been characterized  <cit> .
ash <dig> can function as a part of a histone deactylase complex  and is annotated as being likely to regulate many cell cycle related genes, though those interaction partners are not yet known  <cit> .
results from this analysis suggest that hsp <dig>  pil <dig>  pir <dig>  pir <dig>  ylr049c, and ynl046w might be, directly or via intermediate gene interactions, regulated by ash <dig>  though it is likely that many or most of these proposed novel interactions are false positives, all of them are immediately amenable to direct experimentation.
in figure  <dig>  another set of unreported, but biologically interesting interactions identified by this method is presented.
in this subnetwork, cell cycle related regulatory elements are determined to regulate the expression of two sets of genes: one is annotated to be associated with bud neck and septin checkpoint, and the other is annotated as mannosyltransferases.
these two sets of genes and functions are united by the fact that the cell wall at the bud neck of the dividing yeast cell is derived from mannose-rich glycoproteins.
thus, this method correctly identifies a causal relationship between the regulation of genes that direct the yeast bud neck and the mannosyltransferase complexes that are needed to modify the proteins present at the bud neck.
novelty of the proposed method
the proposed method is robust.
we tested the predictive ability of loi-scores by removing  <dig> published interactions in the 102-gene set from the initial set of published  <dig>  interacting pairs that was used for the calculation of loi-scores for go annotations.
a similar predictive ability of loi-scores for the 102-gene set was confirmed .
our method has a number of advantages over other methods for determining genetic networks.
through the loi-scores, gene interaction pairs that have not been previously identified can be uncovered bounded by biological expectation.
a gene interaction pair need not have been previously observed, but only "be similar" in their go mf annotation to previously observed interaction pairs.
although a q  level of  <dig>  was used to identify interactions here, from tables  <dig> and  <dig> it can be seen that at higher stringencies, superior networks with regard to percent of previously published and percent of interactions sharing a go-bp annotation can be found.
by adjusting the stringency of q , this method can generate smaller networks that are more likely to contain biologically significant interactions.
also the method can be adjusted to give greater weight to the loi-scores or the pccs, depending on a researcher's interest or nature of the data for analysis.
given a dataset of high quality or in a well-characterized system, the pccs might prove most informative.
in a largely unknown system or when the quality of the experimental data is poor, the loi-score might be given greater weight.
it was demonstrated that the addition of biological information improves the results.
this makes this tool appropriate for hypothesis driven analysis of microarray data as the researcher can refine queries of the network by shaping the possibilities with biologically relevant constraints.
the method proposed here is also computationally inexpensive and could easily be scaled up to accommodate much larger datasets, while more mathematically intensive procedures require large amounts of processing time.
alternatively, the method here might be used as an efficient pre-screening of data, limiting the dataset to a smaller set with highly expected biological relevance before the data is given over to the analysis by more mathematically advanced methodologies, e.g.
the bayesian network approach.
choice of go mf annotations
there are over two hundred thousand specific annotations in the go ontology to choose from.
however, the number of yeast genes at a particular annotation is an important factor in the appropriateness of the specific annotations used in this study.
there must be a sufficient number of genes of a given annotation for useful statistical interpretation.
if annotations are too general, resulting loi scores would lack the ability to discriminate against large numbers of possible interaction pairs.
annotations that are too specific will apply only to a handful of genes and not have the ability to identify potentially novel interactions.
the  <dig> sgd go slim mf annotations  <cit> , not including the 'molecular function unknown', range in go annotation level from second to ninth and contain between about less than 1% to about 12% of the yeast genome used here.
lower level go mf terms do not necessarily include larger proportions of the genome.
for example, it is observed for 'translation regulator activity' which is at level two and includes 1% of yeast genes, and 'lyase activity' which is at level three and includes  <dig> % of genes  .
therefore, these  <dig> annotations, which account a small percent of the entire go annotations, are useful for the summarization of go annotations at a genomic level when a broad classification of gene product function is required.
though the  <dig> annotations selected by the curators of the sgd may not be optimized for use in loi-scores, these pre-selected annotations are an excellent place to begin with for the demonstration of the proposed method.
the use of modified pcc
in this paper, the pearson correlation coefficient was computed after applying the alignment based on a 1-time point shift of the gene expression of profile of a target to that of the corresponding regulator.
although this treatment is a simplification of the potential range of possible regulatory mechanisms, it was used to demonstrate that the loi-method can improve on methods that use only expression data.
this may result in underestimation of the performance of our proposed method when considering the common set of predicted interactions from both loi-scores and ppc.
more sophisticated alignment methods for the alignment of temporal profiles  <cit>  may provide a better set of predicted interactions.
our framework for computing loi-scores is general.
loi-scores need not to be limited to either previously published interactions for an initial starting condition or go mf for annotation of gene products.
any current large database of gene interactions could be used as the basis for loi-score calculations and any appropriate gene product annotations could be used.
the possibilities are limited only by the availability of data and ways that the most appropriate datasets and annotations can be applied to specific biological problems.
it is useful to note that the single-celled yeast has a genome that contains around  <dig>  genes.
more complex, multicellular organisms like humans with around  <dig>  genes will posses far more possible interactions and it is likely that finer grained annotations than those used for yeast will be necessary.
CONCLUSIONS
we have proposed a method for generating new hypotheses from microarray data from a single microarray experiment using reported literature in pubmed.
the likelihood of interaction for each pair of go annotations of molecular function selected by the saccharomyces genome database  go slim mapper has been derived from the statistical analysis involved in the reported gene interaction pairs in literature.
combined with the analysis of correlation of microarray expression profiles, it has been demonstrated that the method can uncover existing and novel gene interactions in the analysis of the saccharomyces cerevisiae cell cycle microarray data.
the loi-scores can be also used as a screen for possible gene interacting pairs.
the output from this simple screening can be used as the input for other computationally intensive procedures, e.g.
bayesian network for inferring the gene networks.

 microarrays are routinely used to simultaneously assess the relative expression levels of many thousands of gene transcripts in biological samples. increasingly sophisticated techniques have been developed to detect those transcripts among thousands whose expression levels have significantly changed in response to the selected experimental conditions. yet these lists of differentially expressed genes, however highly refined, are of limited use in themselves. a deeper understanding of the underlying biological mechanisms depends on the identification of how these genes are interacting with the cell. through the development of gene interaction networks, microarray data evolve from being descriptive to being predictive. the use of various statistical methods for identifying significant transcriptional regulatory interactions and for uncovering the corresponding regulatory network structures is a major step toward that direction. however, microarray data themselves do not provide enough information for the definitive identification of regulatory interactions within the cell. the inclusion of other biological information is essential. recently, several groups have proposed methods of constructing regulatory networks based on microarray gene expressions and other large sets of high throughput data. particularly, the methods in can perform analysis of a highly diverse collection of genome-wide data sets, which include gene expression, protein interactions, growth phenotype data, and transcription factor binding, to reveal the modular organization of the yeast system. the identification of regulatory networks at genome scale is important. however, it does not address the issue of analyzing a single set of microarray data obtained from a specific hypothesis-driven experiment. it is possible that the network structure derived based on the above method may be not all observed in a single microarray experiment that was conducted under specific experimental conditions. therefore, it remains an important task for individual investigators to identify the interactions that are most consistent with the data. there are several databases to which a set of genes can be submitted for querying of possible pathways. however, the discovery of new pathways is often not possible by using these databases. this situation has become a major obstacle for deriving new testable hypotheses from a single set of microarray data conducted by an individual research group. on the other hand, the large body of information hidden in the published literature has not been effectively integrated in the microarray data analysis. several groups have attempted to utilize prior biological knowledge to substantially reduce false positives in the outcomes from the data-driven approaches. herrgard et al. have evaluated networks reconciled from gene expression data, known genome-scale regulatory network structures generated from annotated genome information, well-curated databases, and primary research literature. jenssen et al. proposed a gene-to-gene co-citation network for named human genes by an automated analysis of titles and abstract in over million medline records. pathwayassist analysis software by ariadne genomics also provides networks for a set of query genes based on a natural language search algorithm of all available pubmed published abstracts. the limitation of these methods is their inability of discovering novel interactions, since the reported interactions in literature are often used to overlap with the interactions identified from the analysis of microarray data. ideally, a method for determining gene interaction networks would have the ability to use the large amount of prior biological knowledge available to researchers and identify novel gene interactions in a set of microarray expression data for a specific study. the method proposed here attempts to address these two requirements. our method is designed to identify small number of testable hypotheses from a specific microarray data set and collected biological observations. in order to evaluate our method, we define the type of gene interaction considered in this paper as follows. as microarray experiments measure changes in gene expression, the gene interaction used here will be restricted to what might be reasonably expected to be observed in a microarray experiment: a change in the expression of a regulator gene modulates the expression of a target gene. these pairwise interactions form the gene interaction network of a particular system under a particular condition.  datasets the datasets used in this study are subsets of cell-cycle dependent genes in the budding yeast saccharomyces cerevisiae microarray experiments. these microarray experiments were designed to create a comprehensive list of yeast genes whose transcription levels were expressed periodically within the cell cycle. microarrays were prepared for this study by printing pcr-generated probes. in order to study cell cycle related gene expression, the cell cycles of yeast cultures needed to be synchronized. a sample of growth media would contain cells all in the same stage of the cell cycle. the study used for synchronization of the cell cycle is alpha-factor based. alpha-factor, a signaling pheromone, causes cells to undergo cell cycle arrest. pelleting the cells and re-suspending them in fresh media permits normal cell cycle to continue. for the alpha-factor experiment, the gene expressions of cell cycle synchronized yeast cultures were collected over time points taken in 7-minute intervals. this time series covers more than two complete cycles of cell division. two subsets of the data were selected for our study. the first is a subset of genes that includes known transcription regulators and their possible regulatory targets. this set is highly enriched for known interacting genes involved in the saccharomyces cell cycle. the second subset is comprised of of the most cyclically regulated genes in the microarray experiments. in the analysis of a hypothesis-driven experiment, it is neither necessary nor logical to investigate every gene in the genome of an organism. it can be expected that the majority of genes in the genome are irrelevant to the biological phenomenon under investigation. it might be reasonable, for example, to restrict analysis to those genes observed to undergo statistically significant changes or genes that are detected as being significantly expressed. as the data were collected in an experiment to observe genes that are instrumental to directing cell cycle activities, only those genes identified as being cyclically expressed will be used in our analysis. though there is a number of methods that have been used in the past for finding cyclic expression patterns, the method used here most resembles that of filkov et al.. in this, stretches of gene expression data are compared against themselves to look for recurring patterns of expression. this was carried out through the computation of the cyclic correlation coefficient of a gene. see methods section for the definition. the genes with the highest ccc were selected for analysis. the expressions of the genes were identified to fit well with their expected phase in the mitotic cycle. the annotations of these genes were identified to be enriched for annotations specific to cell cycle. identify gene interaction pairs from the likelihood of interaction scores and the modified pearson correlation coefficients one of the goals of this study is to identify gene pairs that are likely to interact on the basis of prior biological knowledge. the likelihood of interaction score is the result of efforts to achieve this goal. using prior information in the pubmed database of scientific publications, information about previously observed gene interactions was collected and used to generate an loi-score for a gene pair. this score was used to determine if a gene pair is likely a potential interaction pair or not. if the gene pair closely resembles gene interaction pairs frequently observed in the literature, it is considered likely and should have a high loi-score; if the gene pair has little similarity to previously observed gene interactions, their interaction is considered unlikely and should have a negative loi-score. loi-scores for all possible pairs of genes from a set of query genes obtained from an experiment will be assigned and arranged in an loi-score matrix. an excel template of the program for calculating loi-scores was made available. on the other hand, a modified pearson correlation coefficient was used to identify the possible gene interaction pairs from the microarray data. a regulation interaction defined in this genetic network is a gene whose change in expression levels has an effect on the subsequent expression of another gene. the 1-time point shift of the putative target gene relative to the regulator gene used here identifies this type of causal relationships in a simplified way. two sets of possible gene interactions are considered significant after the multiple hypothesis testing: one obtained from loi-scores and the other one obtained from pccs. the former set is determined by our prior biological knowledge on a given set of genes collected from scientific literature. the latter set includes statistically significant interactions identified from the analysis of the microarray data from a specific hypothesis-driven experiment. the overlap of these two sets forms a network of gene interactions. these interactions in the network were analyzed to determine how many of the previously published interactions were identified, as well as the number of gene interaction pairs in which both the regulator and the target sharing at least one biological process annotation as provided by the gene ontology slim mapper in saccharomyces genome database. good gene interaction networks are expected to be enriched for both previously published gene interactions and interaction pairs that share go bp annotations. results of using different thresholds q for false positive rate for each set, denoted by loi.fdr and pcc.fdr for loi-scores and pccs respectively, and their common sets with different thresholds are presented below. 102-gene set the results for the 102-gene cell cycle set are summarized in table at pcc.fdr, regulatory interaction pairs are identified, of which had been previously reported in an automated survey of the scientific literature, and interaction pairs share at least one go bp annotation between the regulator and the target. at loi.fdr, regulatory interaction pairs are identified, of which have been previously reported, and of the identified interaction pairs share a go bp annotation between the regulator and target. combining loi.fdr and pcc.fdr, the method generates a network of interaction pairs, of which have been previously reported and of the identified interaction pairs share a go bp annotation between the regulator and target. a: go-simple: a pair of genes is considered potentially interacting if one of their go annotation pairs has been reported. b: set of interactions obtained by applying only pcc thresholds; c: set of interactions obtained by applying only loi thresholds; d: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr; e: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr; f: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr. 999-gene set results for the set of cyclically expressed genes are summarized in table at pcc.fdr, regulatory interaction pairs are identified, of which had been previously reported in an automated survey of the scientific literature, and interaction pairs share at least one go bp annotation between the regulator and the target. at loi.fdr, interaction pairs are identified, of which have been previously reported, and of the identified interaction pairs share a go bp annotation between the regulator and target. the set of interaction pairs with an loi.fdr less than shows slight improvement over that of loi.fdr. combining loi.fdr and pcc.fdr in the 999-gene set, the method generates a network of interaction pairs, of which have been previously reported and of the identified interaction pairs share a go bp annotation between the regulator and target, a significant increase. a: go-simple: a pair of genes is considered potentially interacting if one of their go annotation pairs has been reported. b: set of interactions obtained by applying only pcc thresholds; c: set of interactions obtained by applying only loi thresholds; d: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr; e: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr; f: common set of interactions obtained by applying different loi thresholds and a fixed pcc.fdr. from tables and it can be seen that the total number of previously published interactions remains almost entirely the same at different pcc.fdr when the loi.fdr stringency is fixed at any level. increasing loi.fdr has the effect of reducing the total number of predicted interactions without reducing the total number of previously published interactions. in every case, fixing the loosely restrictive pcc.fdr, the application of increasingly stringent loi.fdr improves the generated interaction network with respect to the percent of identified edges that have been previously published. this can be explained as follows. the loi-scores for most of the published interactions in both sets are associated with substantially lower p-values in comparison to those unpublished pairs. more stringent loi.fdr can eliminate those unpublished pairs with relatively higher p-values for loi-scores without excluding published pairs. the remaining unpublished pairs are likely to have loi-scores with statistically significance and therefore are likely novel interacting pairs. at the first impression, it seems that the 102-gene set performed better than the 999-gene set. there are, however, proportionately far fewer published interactions in the 999-gene set than in the 102-gene set. for the 102-gene set, interaction set determined the thresholds loi.fdr and pcc.fdr has % published interactions, which is about a 4-fold increase over the % published interactions out of all possible interactions. for the 999-gene set, the interaction set determined by the thresholds loi.fdr and pcc.fdr has % published interactions, which is about a 22-fold increase over the % published interactions out of all possible interactions. therefore, the result for the 999-gene set is actually a considerable improvement over the result for the 102-gene set. the method does a better job of finding published interactions in a search space where published interactions are proportionately much sparser. similarly, the distributions of go bp annotations in the 102-gene and 999-gene sets have a significant effect on the generated interaction networks. the 102-gene set was selected to include known cell cycle regulated genes and thus tend to be enriched for a more limited selection of largely cell cycle related go bp annotations. % of all possible gene interaction pairs in the 102-gene set share a go bp annotation. the 999-gene set, on the other hand, was selected solely on the observed experimental data. of all possible interaction pairs in the 999-gene set, there are % interaction pairs that share a go bp annotation. a breakdown of the numbers of shared go bp annotations for the identified interactions in the 999-gene set is shown in table though the 102-gene set appears to perform better, identifying % interaction pairs that share a go bp annotation compared to the 999-gene set with % at loi.fdr and pcc.fdr. again, the method is actually doing a better job on the larger 999-gene set for finding matching go bp annotation interaction pairs from a less enriched background. discussion possible novel interactions in the identified subnetworks in the network identified at loi.fdr and pcc.fdr for the 999-gene set, previously published gene interactions were identified. the majority of these are interactions of transcriptional regulation, specifically the correct identification of transcription factors swi fkh and fhl and several of their regulatory targets. additionally however, regulatory interactions other than simple transcription factors were found, such as kinase regulatory interaction mechanisms like gin and kcc indirectly regulating expression swe. the correct identification of these known interactions adds credence to the possibility that some identified interactions which are not previously reported may be novel discoveries. these potential discoveries should be considered testable hypotheses for future experimentation. there are a number of such potentially novel discoveries in the results from the interaction network of the 999-gene set. some potential transcription factor regulatory interactions, specifically fhl regulating ded and pfk stb regulating cdc and swi regulating cdc all have regulation targets near appropriate transcription factor binding sites, though these interactions have not previously been characterized. ash can function as a part of a histone deactylase complex and is annotated as being likely to regulate many cell cycle related genes, though those interaction partners are not yet known. results from this analysis suggest that hsp pil pir pir ylr049c, and ynl046w might be, directly or via intermediate gene interactions, regulated by ash though it is likely that many or most of these proposed novel interactions are false positives, all of them are immediately amenable to direct experimentation. in figure another set of unreported, but biologically interesting interactions identified by this method is presented. in this subnetwork, cell cycle related regulatory elements are determined to regulate the expression of two sets of genes: one is annotated to be associated with bud neck and septin checkpoint, and the other is annotated as mannosyltransferases. these two sets of genes and functions are united by the fact that the cell wall at the bud neck of the dividing yeast cell is derived from mannose-rich glycoproteins. thus, this method correctly identifies a causal relationship between the regulation of genes that direct the yeast bud neck and the mannosyltransferase complexes that are needed to modify the proteins present at the bud neck. novelty of the proposed method the proposed method is robust. we tested the predictive ability of loi-scores by removing published interactions in the 102-gene set from the initial set of published interacting pairs that was used for the calculation of loi-scores for go annotations. a similar predictive ability of loi-scores for the 102-gene set was confirmed. our method has a number of advantages over other methods for determining genetic networks. through the loi-scores, gene interaction pairs that have not been previously identified can be uncovered bounded by biological expectation. a gene interaction pair need not have been previously observed, but only "be similar" in their go mf annotation to previously observed interaction pairs. although a q level of was used to identify interactions here, from tables and it can be seen that at higher stringencies, superior networks with regard to percent of previously published and percent of interactions sharing a go-bp annotation can be found. by adjusting the stringency of q, this method can generate smaller networks that are more likely to contain biologically significant interactions. also the method can be adjusted to give greater weight to the loi-scores or the pccs, depending on a researcher's interest or nature of the data for analysis. given a dataset of high quality or in a well-characterized system, the pccs might prove most informative. in a largely unknown system or when the quality of the experimental data is poor, the loi-score might be given greater weight. it was demonstrated that the addition of biological information improves the results. this makes this tool appropriate for hypothesis driven analysis of microarray data as the researcher can refine queries of the network by shaping the possibilities with biologically relevant constraints. the method proposed here is also computationally inexpensive and could easily be scaled up to accommodate much larger datasets, while more mathematically intensive procedures require large amounts of processing time. alternatively, the method here might be used as an efficient pre-screening of data, limiting the dataset to a smaller set with highly expected biological relevance before the data is given over to the analysis by more mathematically advanced methodologies, e.g. the bayesian network approach. choice of go mf annotations there are over two hundred thousand specific annotations in the go ontology to choose from. however, the number of yeast genes at a particular annotation is an important factor in the appropriateness of the specific annotations used in this study. there must be a sufficient number of genes of a given annotation for useful statistical interpretation. if annotations are too general, resulting loi scores would lack the ability to discriminate against large numbers of possible interaction pairs. annotations that are too specific will apply only to a handful of genes and not have the ability to identify potentially novel interactions. the sgd go slim mf annotations, not including the'molecular function unknown', range in go annotation level from second to ninth and contain between about less than 1% to about 12% of the yeast genome used here. lower level go mf terms do not necessarily include larger proportions of the genome. for example, it is observed for'translation regulator activity' which is at level two and includes 1% of yeast genes, and'lyase activity' which is at level three and includes % of genes. therefore, these annotations, which account a small percent of the entire go annotations, are useful for the summarization of go annotations at a genomic level when a broad classification of gene product function is required. though the annotations selected by the curators of the sgd may not be optimized for use in loi-scores, these pre-selected annotations are an excellent place to begin with for the demonstration of the proposed method. the use of modified pcc in this paper, the pearson correlation coefficient was computed after applying the alignment based on a 1-time point shift of the gene expression of profile of a target to that of the corresponding regulator. although this treatment is a simplification of the potential range of possible regulatory mechanisms, it was used to demonstrate that the loi-method can improve on methods that use only expression data. this may result in underestimation of the performance of our proposed method when considering the common set of predicted interactions from both loi-scores and ppc. more sophisticated alignment methods for the alignment of temporal profiles may provide a better set of predicted interactions. our framework for computing loi-scores is general. loi-scores need not to be limited to either previously published interactions for an initial starting condition or go mf for annotation of gene products. any current large database of gene interactions could be used as the basis for loi-score calculations and any appropriate gene product annotations could be used. the possibilities are limited only by the availability of data and ways that the most appropriate datasets and annotations can be applied to specific biological problems. it is useful to note that the single-celled yeast has a genome that contains around genes. more complex, multicellular organisms like humans with around genes will posses far more possible interactions and it is likely that finer grained annotations than those used for yeast will be necessary.  we have proposed a method for generating new hypotheses from microarray data from a single microarray experiment using reported literature in pubmed. the likelihood of interaction for each pair of go annotations of molecular function selected by the saccharomyces genome database go slim mapper has been derived from the statistical analysis involved in the reported gene interaction pairs in literature. combined with the analysis of correlation of microarray expression profiles, it has been demonstrated that the method can uncover existing and novel gene interactions in the analysis of the saccharomyces cerevisiae cell cycle microarray data. the loi-scores can be also used as a screen for possible gene interacting pairs. the output from this simple screening can be used as the input for other computationally intensive procedures, e.g. bayesian network for inferring the gene networks. 
the loi-score and the pearson correlation coefficient of gene profiles were utilized to check if a pair of query genes would be in the above specified interaction. however, individual researchers are more interested in the extraction of most useful information from the data of their hypothesis-driven microarray experiments. how to compile the prior biological knowledge from literature to facilitate new hypothesis generation from a microarray experiment is the focus of this work. the incorporation of prior biological knowledge in the analysis of microarray data has become important in the reconstruction of transcription regulatory networks in a cell. most of the current research has been focused on the integration of multiple sets of microarray data as well as curated databases for a genome scale reconstruction. the method was validated in the analysis of two gene sets formed from the yeast saccharomyces cerevisiae cell cycle microarray data. we propose a novel method based on the statistical analysis of reported gene interactions in pubmed literature. using gene ontology molecular function annotation for reported gene regulatory interactions in pubmed literature, a statistical analysis method was proposed for the derivation of a likelihood of interaction score for a pair of genes. this method can uncover novel biologically relevant gene interactions.
 the incorporation of prior biological knowledge in the analysis of microarray data has become important in the reconstruction of transcription regulatory networks in a cell. most of the current research has been focused on the integration of multiple sets of microarray data as well as curated databases for a genome scale reconstruction. however, individual researchers are more interested in the extraction of most useful information from the data of their hypothesis-driven microarray experiments. how to compile the prior biological knowledge from literature to facilitate new hypothesis generation from a microarray experiment is the focus of this work. we propose a novel method based on the statistical analysis of reported gene interactions in pubmed literature.  using gene ontology molecular function annotation for reported gene regulatory interactions in pubmed literature, a statistical analysis method was proposed for the derivation of a likelihood of interaction score for a pair of genes. the loi-score and the pearson correlation coefficient of gene profiles were utilized to check if a pair of query genes would be in the above specified interaction. the method was validated in the analysis of two gene sets formed from the yeast saccharomyces cerevisiae cell cycle microarray data. it was found that high percentage of identified interactions shares go biological process annotations.  this method can uncover novel biologically relevant gene interactions. with stringent confidence levels, small interaction networks can be identified for further establishment of a hypothesis testable by biological experiment. this procedure is computationally inexpensive and can be used as a preprocessing procedure for screening potential biologically relevant gene pairs subject to the analysis with sophisticated statistical methods. 