BACKGROUND
progress in the life sciences has recently been made by integrating biomedical knowledge on numerous genes and formulating hypotheses on the genetic mechanisms underlying various vital phenomena  <cit> .
a large variety of genetic and biomedical knowledge on genes has been compiled into databases  <cit> , and is available in electronic forms such as the online mendelian inheritance in man  database  <cit> .
researchers and physicians formulating hypotheses often need to identify groups of functionally related genes, such as gene families and gene pathways, and this is usually done by simply reading a large number of documents related to the phenomenon of interest  <cit> .
since such an approach will inevitably result in some relevant literature being overlooked, researchers and physicians need a way that will help them search for related gene sets automatically and comprehensively  <cit> .
graph-based approaches  <cit>  have recently emerged as a method for data mining.
a biomedical relational graph is formed by nodes that represent biological entities  and edges that represent the associations of those entities.
for instance, protein-protein interactions are modeled by a graph, where nodes are proteins and two nodes are connected by an edge if the corresponding proteins physically bind.
protein functions are predicted using connections in a graph  <cit>  based on the assumption that proteins which lie close to one another are more likely to have similar functions or constitute protein complexes.
for extracting coherent groups of genes as modules, a module-assisted approach  <cit>  has been introduced.
prior studies include attempts to extract modules from protein-protein interactions  <cit> , co-expression in microarray data  <cit> , and gene symbol co-occurrence in medline article abstracts  <cit> .
computational tools for visualizing modules  in a given graph  <cit>  have been developed.
here we have constructed a biomedical relational graph whose nodes are pages of genes or diseases and whose edges are hyperlink connections between pages by using over  <dig>  entities in the omim database  <cit> .
the omim database, which is a biomedical database of human genes and genetic disorders, contains a great number of relationships between genes and diseases.
this work was based on the assumption that the structures of hyperlink connections correspond to the structural features of biological systems.
clique-based data mining has been applied to a relational graph based on the assumption that relevant relationships are reflected in completely interconnected subgraphs  or nearly completely interconnected subgraphs .
we address the extraction of related genes  by searching for densely connected subgraphs in a biomedical relational graph.
sets of related genes are detected by enumerating densely-connected subgraphs modeled as cliques  <cit>  or pseudo-cliques  <cit> .
using this method, we extracted over  <dig>  gene modules.
to the best of our knowledge, this is the first study to show that sets of related genes can be comprehensively extracted from a biomedical database and that these related genes can be utilized to gain insight into the mechanisms of complex diseases.
methods
materials
the experimental materials were taken from the online mendelian inheritance in man  database, a well-known catalog in which human genes and genetic disorders are assigned descriptive code numbers  <cit> .
using these numbers, they are connected by hyperlinks according to their associations, such as physical proximity, similarity of nomencalture or structure, or functional association.
kegg pathway data that contains sets of genes were obtained  from a database produced by the cancer genome anatomy project  <cit> .
clique enumeration
an undirected graph g consists of a set v of nodes and a set e of non-weighted edges connecting pairs of nodes.
the number of edges connected to node v ∈ v is referred to as the degree of node v in g. the subgraph of g is induced by the subset v' of v. a subgraph in which every pair of nodes is connected by an edge  is called a clique, and the size of which is the number of nodes in it.
a clique is called maximal if it is included in no other clique.
pseudo-cliques are the subgraphs obtained by relaxing the connectivities, and the connectivity is measured by the edge density ρ. the edge density of a pseudo-clique s is the ratio of the number of edges in s to the number of edges in a clique that has the same number of nodes that s does.
it is calculated as follows:
where |e| is the number of edges in s and |s| is the number of nodes in s. by setting ρ to a threshold value θ  pseudo-cliques whose edge density is not less than θ can be enumerated  <cit> .
as a θ decreases from  <dig> to  <dig>  we get pseudo-cliques whose connectivity is more and more relaxed.
in the work presented in this paper we obtained gene modules  by enumerating pseudo-cliques in a graph whose node were genes and genetic disorders and whose connecting edges were biomedical relations.
correspondence analysis
correspondence analysis  <cit>  is a method to analyze relations between categorical variables, called cases and items.
this analysis yields an arrangement in which similar cases and items are closely placed.
by introducing a data matrix whose rows and columns are variables  having values of  <dig> or  <dig> at components depending on the absence or presence of the relations between the variables, rows  and columns  are arranged by sorting the scores calculated using the second largest eigenvalue and the corresponding eigenvector.
in the work presented in this paper items and cases are assigned respectively to modules and genes/diseases contained.
RESULTS
an example: enumerating cliques
we will present an example of enumerating cliques from a part of an undirected graph used in experiments described later in this paper.
this enables us to see clique enumeration from a biomedical relational graph and to investigate the validity in an accessible fashion.
the node sets for all the maximal cliques obtained from the graph in figure  <dig> are listed in table  <dig>  it is expected that there are many cliques that contain the hypertension node.
the extraction of cliques whose nodes include hypertension and the agt, ace, agtr <dig>  and ren genes, is in agreement with the medical knowledge.
the renin-angiotensin system in which the ren, agt, ace, and agtr <dig> genes interact with each other  <cit>  is the well known pressor mechanism that acts in concert with the cyp11b <dig> and cyp11b <dig> genes related to aldosterone secretion.
the extraction of cliques whose nodes include the cyp <dig> and hsd11b <dig> genes in addition to the cyp11b <dig> and cyp11b <dig> genes is consistent with the reported interaction of their enzymes with aldosterone synthesis  <cit> .
related genes that regulate each other can thus be extracted by enumerating cliques in biomedical relational graphs.
biomedical relational graph by omim hyperlinks
we constructed an undirected graph whose nodes were gene or disease pages in the omim database and whose edges were hyperlink connections between those pages.
as shown by the data listed in table  <dig>  the graph had  <dig>  nodes and  <dig>  edges .
each hyperlink connection was counted only once.
we limited this study to  <dig>  genes both in the omim database and in swiss-prot  <cit>  , excluding genes with no medline citations or no gene locus descriptions or no hyperlinks to other omim pages.
the characteristic path length is defined as the number of edges in the shortest path between two nodes, averaged over all pairs of nodes.
the clustering coefficient measures the average degree of node coherence connected by the edges.
when a node v has kv connected nodes, the clustering coefficient is the ratio of the actual number of edges to the possible kv/ <dig> edges.
the characteristic path length and clustering coefficient  <cit>  were respectively  <dig>  and  <dig>  .
these values indicate that the graph contained clusters of densely-connected nodes and that there were hub-like nodes connecting the clusters.
extraction of gene modules by searching cliques
in the graph were  <dig>  maximal cliques and the largest maximal clique contained  <dig> nodes.
the  <dig> most frequent genes in the maximal cliques are listed in table  <dig> along with their degrees and the number of times they were found in cliques of various sizes.
the nfkb <dig> gene  was also in five cliques of size  <dig>  the brca <dig> gene  in one clique of size  <dig>  and the taf <dig> gene  in one clique of size  <dig>  the genes found most frequently in the maximal cliques  are typical genes that play a central role and are prevalent research subjects.
the clique distribution of the taf <dig> gene  is relatively shifted towards larger sizes, suggesting it forms complexes with some gene products since the gene function is a transcription factor.
also listed for each gene is its degree and the number of times it was found in cliques of various sizes.
when pseudo-cliques were extracted by the relaxing edge connecting condition, the maximum sizes increased as θ decreased .
preliminary experimental results showed that gene modules should consist of possible related genes for the analysis.
by relaxing connectivities , pseudo-cliques were introduced for gene modules to collect the possible related genes.
the maximum pseudo-clique size reached  <dig> when θ =  <dig>  and did not increase further even when θ was decreased to  <dig>  .
for this reason,  <dig>  pseudo-cliques  were taken as gene modules in the following analysis.
the sensitivity and specificity of the current method in extracting biologically relevant genes using cliques can not be readily assessed.
one would assume that cliques with larger size are likely to be more biologically relevant.
to account for this, we estimated whether a cutoff value on the clique size can be set.
we examined the maximum size of cliques in randomized graphs having common node degrees to the omim hyperlink graph.
in the experiments of enumerating pseudo-cliques  in the randomized graphs which were generated using an edge-swapping approach  <cit> , the maximum size four was observed .
this result suggests that clique size four may be considered as a background clique size, and that clique size of more than four may be biologically relevant.
while we employed the clique enumeration method to extract sets of related genes, application of the method by the edge-betweenness clustering  <cit> , which introduces the concept of graph modularity called community, may also be considered.
basically the community-based approach is conducted with graph partitioning to separate connected nodes into groups of nodes that have a high density of edges within them, with a lower density of edges between groups  <cit> .
in contrast, the clique-based approach allows for any node to belong to more than one group of nodes.
it would be interesting to compare the performance of these different methods and investigate how to use the methods properly.
biological evaluation of extracted gene modules
to evaluate how well the gene modules correspond to known gene pathways, we compared the sets of genes in the modules with those in the kegg pathway database  <cit> .
the  <dig> kegg pathways that contain more than five genes and the  <dig>  gene modules  were compared by calculating values of the jaccard's coefficient rj .
the jaccard's coefficient is often used as a criterion when evaluating the similarity of two sets, and is the ratio of the size of the intersection of the sets to the size of the union of the sets.
its value thus approaches  <dig> as the extent of coincidence increases.
the  <dig> most relevant kegg pathways and the corresponding gene module sizes are listed in table  <dig>  where the numbers in the square brackets are numbers of genes in the kegg pathway.
for instance, in the top rank of kegg pathway 'blood group glycolipid biosynthesis,' the number of genes contained was six.
the corresponding gene module by a pseudo-clique included four genes.
as for the other kegg pathways, none of the gene module sizes equaled the number of genes in the kegg pathway.
this implies that a pseudo-clique can partially extract genes included in biological pathways.
in order to evaluate how well the gene modules accord with interactions between genes, we compared the sets of genes in the modules with gene pairs in the protein-protein interaction data  <cit> .
the  <dig> interactions for the above-mentioned  <dig>  genes were obtained from the protein-protein interaction data.
among the  <dig> gene pairs  <dig> pairs were captured in the gene modules, which correspond to  <dig> % of the total interactions.
the gene pairs in the protein-protein interactions which were repeatedly captured by gene modules are listed in table  <dig>  the gene pair in the first row is involved in genes of biological pathways related to mapk signaling and apoptosis.
the second pair is involved in tgf-β signaling.
the third is in regulation of food intake and energy monitoring.
repeated captures suggest that the pairs have various functions in biological setting.
analysis of gene relationships using 'metabolic syndrome'-related gene modules
for applying gene modules to disease mechanism analysis, we assembled gene modules associated with the metabolic syndrome  <cit>  as an example of a typical multifactorial disease.
the metabolic syndrome is a heterogeneous disease characterized by the onset and progession of four common disorders: obesity, diabetes, hyperlipidemia, and hypertension.
the genes associated with these disorders might interact with each other and lead to arteriosclerotic diseases such as myocardial infarction or ischemic stroke.
public attention has been focused on its prevention  <cit> .
we examined the congruence with current medical knowledge.
gene modules associated with diabetes, hyperlipidemia, hypertension, and obesity are obtained by their containing the disease nodes, which are non-insulin-dependent diabetes mellitus , familial combined hyperlipidemia , essential hypertension , and obesity .
out of  <dig>  modules,  <dig>   <dig>   <dig>  and  <dig> modules are obtained, respectively.
there were no overlaps among the modules.
then a total of  <dig> modules and  <dig> genes contained were identified.
the  <dig> most frequent genes in the  <dig> modules are listed in table  <dig> along with the numbers of times they were found in the modules  of various sizes.
as shown in the table, ins gene and lep gene are the top and the 2nd, respectively.
the modules of size  <dig> including ins gene or lep gene were {obesity, lep, mc4r, pomc, agrp, lepr}, {obesity, lep, mc4r, pomc, agrp, pcsk1}, {diabetes, lep, igf <dig>  irs <dig>  ins, irs2}.
each module contains biologically plausible genes related to obesity or diabetes.
we combined the  <dig> modules and  <dig> genes using correspondence analysis .
each bar in figure  <dig> corresponds to a gene node or disease node in the cliques, and the names of the genes or diseases are shown on the right.
each bar is colored according to the disease nodes included in the corresponding module: red for hypertension, gray for hyperlipidemia, blue for obesity, and green for diabetes.
the letters of 't,' 'l,' 'o,' and 'd' to the right of the figure respectively indicate that in the literature the genes in the corresponding rows are related to hypertension  <cit> , hypertrigricedemia  <cit> , obesity  <cit> , and diabetes  <cit> .
hyperlipidemia-related genes are replaced by genes associated with hypertrigricedemia .
as shown in figure  <dig>  the gene modules associated with the four diseases occupy their own regions from the top-left to the bottom-right.
as for the disease nodes shown to be belt-like in the figure, the nodes of hypertension, hyperlipidemia, obesity, diabetes are placed from the top to the middle.
there is a diabetes node at the middle and the letter 'd' indicating diabetes-related genes are mostly clustered in the lower half.
this implies that gene modules properly reflect the biological functions of the corresponding genes and that the modules can provide a holistic view of a complex disease.
correspondence analysis has been successfully applied to combine gene modules for interpreting relationships among genes with their gradation on relation to diseases.
genes related to the above-mentioned aldosterone metabolism  are at the top of the figure.
genes related to the renin-angiotensin system  are around the hypertension node indicated by the cluster of 't' marks, and apolipoprotein genes  are gathered around the hyperlipidemia node close to the 'renin-angiotensin system'-related genes.
it is worth noting that 'food intake regulation'-related genes such as lep, npy, mc4r, agrp and pomc genes are grouped comprehensively .
insulin resistance is defined as a status in which the action of insulin is insufficient and proper energy conversion is impaired.
the tnf gene related to insulin resistance  <cit> , which is a shared foundation of the four disorders, was in modules associated with hypertension, obesity, and diabetes, indicating that it has a variety of effects on the development of metabolic syndrome.
it would be intriguing to find the crh gene, which has a biological function in stress responses based on the hypothalamic-pituitary-adrenal axis, in the vicinity of the 'food intake regulation'-related gene region.
fgf -family genes appear at the bottom of the figure, indicating their relevance to diabetes.
although fgf-genes are associated with cancers, inflammation has been investigated in both cancer and metabolic syndrome.
such findings in this chart suggest innovative avenues of research.
obtaining the comprehensive list of related genes makes it possible to combine the gene modules and grasp relations among genes quantitatively, facilitating hypothesis formulation from a holistic viewpoint.
in principle, the biological functions of desired genes as well as disease-related genes could be grasped by combining modules that contain those genes.
CONCLUSIONS
we have shown that related genes can be extracted comprehensively by enumerating pseudo-cliques in biomedical relational graph.
over  <dig>  gene modules that include genes in the same family, genes encoding proteins in the same complexes, and genes encoding components of the same signaling pathway were extracted automatically.
furthermore these gene modules were utilized for visualizing relations between genes and diseases.
extraction of related genes  would be more important since most biomedical tasks are performed not by individual genes, but by sets of functionally associated genes.
the method using pseudo-cliques can generally extract related gene sets under a single computational operation although the application is restricted to genes connected by their relations in the manner of the omim hyperlink connections.
for clarifying complex disease mechanisms, obtaining relationships among genes associated with the diseases should be crucial.
comprehensive extraction of related-gene sets by clique-based data mining may provide us with a systematic methodology for gaining insight into the genetic mechanisms underlying various biological phenomena including diseases.
authors' contributions
tm and et conceived and designed the experiments.
tm and cy performed the experiments.
tm and mm analyzed the data.
all authors have read and approved the final manuscript.

 progress in the life sciences has recently been made by integrating biomedical knowledge on numerous genes and formulating hypotheses on the genetic mechanisms underlying various vital phenomena. a large variety of genetic and biomedical knowledge on genes has been compiled into databases, and is available in electronic forms such as the online mendelian inheritance in man database. researchers and physicians formulating hypotheses often need to identify groups of functionally related genes, such as gene families and gene pathways, and this is usually done by simply reading a large number of documents related to the phenomenon of interest. since such an approach will inevitably result in some relevant literature being overlooked, researchers and physicians need a way that will help them search for related gene sets automatically and comprehensively. graph-based approaches have recently emerged as a method for data mining. a biomedical relational graph is formed by nodes that represent biological entities and edges that represent the associations of those entities. for instance, protein-protein interactions are modeled by a graph, where nodes are proteins and two nodes are connected by an edge if the corresponding proteins physically bind. protein functions are predicted using connections in a graph based on the assumption that proteins which lie close to one another are more likely to have similar functions or constitute protein complexes. for extracting coherent groups of genes as modules, a module-assisted approach has been introduced. prior studies include attempts to extract modules from protein-protein interactions, co-expression in microarray data, and gene symbol co-occurrence in medline article abstracts. computational tools for visualizing modules in a given graph have been developed. here we have constructed a biomedical relational graph whose nodes are pages of genes or diseases and whose edges are hyperlink connections between pages by using over entities in the omim database. the omim database, which is a biomedical database of human genes and genetic disorders, contains a great number of relationships between genes and diseases. this work was based on the assumption that the structures of hyperlink connections correspond to the structural features of biological systems. clique-based data mining has been applied to a relational graph based on the assumption that relevant relationships are reflected in completely interconnected subgraphs or nearly completely interconnected subgraphs. we address the extraction of related genes by searching for densely connected subgraphs in a biomedical relational graph. sets of related genes are detected by enumerating densely-connected subgraphs modeled as cliques or pseudo-cliques. using this method, we extracted over gene modules. to the best of our knowledge, this is the first study to show that sets of related genes can be comprehensively extracted from a biomedical database and that these related genes can be utilized to gain insight into the mechanisms of complex diseases. methods materials the experimental materials were taken from the online mendelian inheritance in man database, a well-known catalog in which human genes and genetic disorders are assigned descriptive code numbers. using these numbers, they are connected by hyperlinks according to their associations, such as physical proximity, similarity of nomencalture or structure, or functional association. kegg pathway data that contains sets of genes were obtained from a database produced by the cancer genome anatomy project. clique enumeration an undirected graph g consists of a set v of nodes and a set e of non-weighted edges connecting pairs of nodes. the number of edges connected to node v ∈ v is referred to as the degree of node v in g. the subgraph of g is induced by the subset v' of v. a subgraph in which every pair of nodes is connected by an edge is called a clique, and the size of which is the number of nodes in it. a clique is called maximal if it is included in no other clique. pseudo-cliques are the subgraphs obtained by relaxing the connectivities, and the connectivity is measured by the edge density ρ. the edge density of a pseudo-clique s is the ratio of the number of edges in s to the number of edges in a clique that has the same number of nodes that s does. it is calculated as follows: where |e| is the number of edges in s and |s| is the number of nodes in s. by setting ρ to a threshold value θ pseudo-cliques whose edge density is not less than θ can be enumerated. as a θ decreases from to we get pseudo-cliques whose connectivity is more and more relaxed. in the work presented in this paper we obtained gene modules by enumerating pseudo-cliques in a graph whose node were genes and genetic disorders and whose connecting edges were biomedical relations. correspondence analysis correspondence analysis is a method to analyze relations between categorical variables, called cases and items. this analysis yields an arrangement in which similar cases and items are closely placed. by introducing a data matrix whose rows and columns are variables having values of or at components depending on the absence or presence of the relations between the variables, rows and columns are arranged by sorting the scores calculated using the second largest eigenvalue and the corresponding eigenvector. in the work presented in this paper items and cases are assigned respectively to modules and genes/diseases contained.  an example: enumerating cliques we will present an example of enumerating cliques from a part of an undirected graph used in experiments described later in this paper. this enables us to see clique enumeration from a biomedical relational graph and to investigate the validity in an accessible fashion. the node sets for all the maximal cliques obtained from the graph in figure are listed in table it is expected that there are many cliques that contain the hypertension node. the extraction of cliques whose nodes include hypertension and the agt, ace, agtr and ren genes, is in agreement with the medical knowledge. the renin-angiotensin system in which the ren, agt, ace, and agtr genes interact with each other is the well known pressor mechanism that acts in concert with the cyp11b and cyp11b genes related to aldosterone secretion. the extraction of cliques whose nodes include the cyp and hsd11b genes in addition to the cyp11b and cyp11b genes is consistent with the reported interaction of their enzymes with aldosterone synthesis. related genes that regulate each other can thus be extracted by enumerating cliques in biomedical relational graphs. biomedical relational graph by omim hyperlinks we constructed an undirected graph whose nodes were gene or disease pages in the omim database and whose edges were hyperlink connections between those pages. as shown by the data listed in table the graph had nodes and edges. each hyperlink connection was counted only once. we limited this study to genes both in the omim database and in swiss-prot, excluding genes with no medline citations or no gene locus descriptions or no hyperlinks to other omim pages. the characteristic path length is defined as the number of edges in the shortest path between two nodes, averaged over all pairs of nodes. the clustering coefficient measures the average degree of node coherence connected by the edges. when a node v has kv connected nodes, the clustering coefficient is the ratio of the actual number of edges to the possible kv/ edges. the characteristic path length and clustering coefficient were respectively and. these values indicate that the graph contained clusters of densely-connected nodes and that there were hub-like nodes connecting the clusters. extraction of gene modules by searching cliques in the graph were maximal cliques and the largest maximal clique contained nodes. the most frequent genes in the maximal cliques are listed in table along with their degrees and the number of times they were found in cliques of various sizes. the nfkb gene was also in five cliques of size the brca gene in one clique of size and the taf gene in one clique of size the genes found most frequently in the maximal cliques are typical genes that play a central role and are prevalent research subjects. the clique distribution of the taf gene is relatively shifted towards larger sizes, suggesting it forms complexes with some gene products since the gene function is a transcription factor. also listed for each gene is its degree and the number of times it was found in cliques of various sizes. when pseudo-cliques were extracted by the relaxing edge connecting condition, the maximum sizes increased as θ decreased. preliminary experimental results showed that gene modules should consist of possible related genes for the analysis. by relaxing connectivities, pseudo-cliques were introduced for gene modules to collect the possible related genes. the maximum pseudo-clique size reached when θ = and did not increase further even when θ was decreased to. for this reason, pseudo-cliques were taken as gene modules in the following analysis. the sensitivity and specificity of the current method in extracting biologically relevant genes using cliques can not be readily assessed. one would assume that cliques with larger size are likely to be more biologically relevant. to account for this, we estimated whether a cutoff value on the clique size can be set. we examined the maximum size of cliques in randomized graphs having common node degrees to the omim hyperlink graph. in the experiments of enumerating pseudo-cliques in the randomized graphs which were generated using an edge-swapping approach, the maximum size four was observed. this result suggests that clique size four may be considered as a background clique size, and that clique size of more than four may be biologically relevant. while we employed the clique enumeration method to extract sets of related genes, application of the method by the edge-betweenness clustering, which introduces the concept of graph modularity called community, may also be considered. basically the community-based approach is conducted with graph partitioning to separate connected nodes into groups of nodes that have a high density of edges within them, with a lower density of edges between groups. in contrast, the clique-based approach allows for any node to belong to more than one group of nodes. it would be interesting to compare the performance of these different methods and investigate how to use the methods properly. biological evaluation of extracted gene modules to evaluate how well the gene modules correspond to known gene pathways, we compared the sets of genes in the modules with those in the kegg pathway database. the kegg pathways that contain more than five genes and the gene modules were compared by calculating values of the jaccard's coefficient rj. the jaccard's coefficient is often used as a criterion when evaluating the similarity of two sets, and is the ratio of the size of the intersection of the sets to the size of the union of the sets. its value thus approaches as the extent of coincidence increases. the most relevant kegg pathways and the corresponding gene module sizes are listed in table where the numbers in the square brackets are numbers of genes in the kegg pathway. for instance, in the top rank of kegg pathway'blood group glycolipid biosynthesis,' the number of genes contained was six. the corresponding gene module by a pseudo-clique included four genes. as for the other kegg pathways, none of the gene module sizes equaled the number of genes in the kegg pathway. this implies that a pseudo-clique can partially extract genes included in biological pathways. in order to evaluate how well the gene modules accord with interactions between genes, we compared the sets of genes in the modules with gene pairs in the protein-protein interaction data. the interactions for the above-mentioned genes were obtained from the protein-protein interaction data. among the gene pairs pairs were captured in the gene modules, which correspond to % of the total interactions. the gene pairs in the protein-protein interactions which were repeatedly captured by gene modules are listed in table the gene pair in the first row is involved in genes of biological pathways related to mapk signaling and apoptosis. the second pair is involved in tgf-β signaling. the third is in regulation of food intake and energy monitoring. repeated captures suggest that the pairs have various functions in biological setting. analysis of gene relationships using'metabolic syndrome'-related gene modules for applying gene modules to disease mechanism analysis, we assembled gene modules associated with the metabolic syndrome as an example of a typical multifactorial disease. the metabolic syndrome is a heterogeneous disease characterized by the onset and progession of four common disorders: obesity, diabetes, hyperlipidemia, and hypertension. the genes associated with these disorders might interact with each other and lead to arteriosclerotic diseases such as myocardial infarction or ischemic stroke. public attention has been focused on its prevention. we examined the congruence with current medical knowledge. gene modules associated with diabetes, hyperlipidemia, hypertension, and obesity are obtained by their containing the disease nodes, which are non-insulin-dependent diabetes mellitus, familial combined hyperlipidemia, essential hypertension, and obesity. out of modules, and modules are obtained, respectively. there were no overlaps among the modules. then a total of modules and genes contained were identified. the most frequent genes in the modules are listed in table along with the numbers of times they were found in the modules of various sizes. as shown in the table, ins gene and lep gene are the top and the 2nd, respectively. the modules of size including ins gene or lep gene were {obesity, lep, mc4r, pomc, agrp, lepr}, {obesity, lep, mc4r, pomc, agrp, pcsk1}, {diabetes, lep, igf irs ins, irs2}. each module contains biologically plausible genes related to obesity or diabetes. we combined the modules and genes using correspondence analysis. each bar in figure corresponds to a gene node or disease node in the cliques, and the names of the genes or diseases are shown on the right. each bar is colored according to the disease nodes included in the corresponding module: red for hypertension, gray for hyperlipidemia, blue for obesity, and green for diabetes. the letters of't,''l,''o,' and'd' to the right of the figure respectively indicate that in the literature the genes in the corresponding rows are related to hypertension, hypertrigricedemia, obesity, and diabetes. hyperlipidemia-related genes are replaced by genes associated with hypertrigricedemia. as shown in figure the gene modules associated with the four diseases occupy their own regions from the top-left to the bottom-right. as for the disease nodes shown to be belt-like in the figure, the nodes of hypertension, hyperlipidemia, obesity, diabetes are placed from the top to the middle. there is a diabetes node at the middle and the letter'd' indicating diabetes-related genes are mostly clustered in the lower half. this implies that gene modules properly reflect the biological functions of the corresponding genes and that the modules can provide a holistic view of a complex disease. correspondence analysis has been successfully applied to combine gene modules for interpreting relationships among genes with their gradation on relation to diseases. genes related to the above-mentioned aldosterone metabolism are at the top of the figure. genes related to the renin-angiotensin system are around the hypertension node indicated by the cluster of't' marks, and apolipoprotein genes are gathered around the hyperlipidemia node close to the'renin-angiotensin system'-related genes. it is worth noting that'food intake regulation'-related genes such as lep, npy, mc4r, agrp and pomc genes are grouped comprehensively. insulin resistance is defined as a status in which the action of insulin is insufficient and proper energy conversion is impaired. the tnf gene related to insulin resistance, which is a shared foundation of the four disorders, was in modules associated with hypertension, obesity, and diabetes, indicating that it has a variety of effects on the development of metabolic syndrome. it would be intriguing to find the crh gene, which has a biological function in stress responses based on the hypothalamic-pituitary-adrenal axis, in the vicinity of the'food intake regulation'-related gene region. fgf -family genes appear at the bottom of the figure, indicating their relevance to diabetes. although fgf-genes are associated with cancers, inflammation has been investigated in both cancer and metabolic syndrome. such findings in this chart suggest innovative avenues of research. obtaining the comprehensive list of related genes makes it possible to combine the gene modules and grasp relations among genes quantitatively, facilitating hypothesis formulation from a holistic viewpoint. in principle, the biological functions of desired genes as well as disease-related genes could be grasped by combining modules that contain those genes.  we have shown that related genes can be extracted comprehensively by enumerating pseudo-cliques in biomedical relational graph. over gene modules that include genes in the same family, genes encoding proteins in the same complexes, and genes encoding components of the same signaling pathway were extracted automatically. furthermore these gene modules were utilized for visualizing relations between genes and diseases. extraction of related genes would be more important since most biomedical tasks are performed not by individual genes, but by sets of functionally associated genes. the method using pseudo-cliques can generally extract related gene sets under a single computational operation although the application is restricted to genes connected by their relations in the manner of the omim hyperlink connections. for clarifying complex disease mechanisms, obtaining relationships among genes associated with the diseases should be crucial. comprehensive extraction of related-gene sets by clique-based data mining may provide us with a systematic methodology for gaining insight into the genetic mechanisms underlying various biological phenomena including diseases. authors' contributions tm and et conceived and designed the experiments. tm and cy performed the experiments. tm and mm analyzed the data. all authors have read and approved the final manuscript.
progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph. we constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the online mendelian inheritance in man database. the results of experiments using'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes. the extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms.
 progress in the life sciences cannot be made without integrating biomedical knowledge on numerous genes in order to help formulate hypotheses on the genetic mechanisms behind various biological phenomena, including diseases. there is thus a strong need for a way to automatically and comprehensively search from biomedical databases for related genes, such as genes in the same families and genes encoding components of the same pathways. here we address the extraction of related genes by searching for densely-connected subgraphs, which are modeled as cliques, in a biomedical relational graph.  we constructed a graph whose nodes were gene or disease pages, and edges were the hyperlink connections between those pages in the online mendelian inheritance in man database. we obtained over sets of related genes by enumerating cliques computationally. the modules included genes in the same family, genes for proteins that form a complex, and genes for components of the same signaling pathway. the results of experiments using'metabolic syndrome'-related gene modules show that the gene modules can be used to get a coherent holistic picture helpful for interpreting relations among genes.  we presented a data mining approach extracting related genes by enumerating cliques. the extracted gene sets provide a holistic picture useful for comprehending complex disease mechanisms. 