cardioembolic stroke ( ces ) , which causes 20% of all ischemic strokes each year , leads to severe neurological deficits . ces is associated with high mortality and is a common cause of its atrial fibrillation ( af ) , which has an increasing incidence with age [ 35 ] . proposed that af is an important and treatable cause of recurrent stroke and needs to be ruled - out by thorough evaluation before the diagnosis of cryptogenic stroke is assigned . ces is largely preventable through control of major primary cardioembolic risk factors , such as hyperlipidemia and high blood pressure . giralt et al .   offered evidence of significant genetic involvement in ischemic stroke . in recent years , gene expression profiling of human disease tissues has provided insights into molecular mechanisms and eventually led to the identification of novel therapeutic targets . currently available high - throughput microarray experiments were developed to analyze genetic expression patterns with differentially expressed genes ( deg ) and dysregulated pathways . canonical reports claimed that gene expression patterns can identify biomarkers of ischemic stroke , which highlighted the relevance of the innate immune system through deg   and signaling pathways [ 1113 ] . however , most methods did not consider regulatory cross - talk among pathways , and treated pathways as independent mechanisms . although it is intuitive that interacting pathways could influence each other , the presence of this frame and available technique have not been completely studied yet . antonio et al .   developed an integrated approach to identify functional mirnas regulating pathway cross - talk in breast cancer with pairs of pathways . differential protein - protein interaction networks were constructed in ces with akaike information criterion ( aic ) method . to the best of our knowledge , there are few studies that constructed pathway networks correctly to discriminate controls versus ces . in this work we develop an integrated approach that is able to construct individual networks comprising pathways cross - talk to quantify differences between ces and controls . we used the individualized pathway aberrance score ( ipas ) to assess pathway statistics of every ingenuity pathways analysis ( ipa ) pathway . random forest ( rf ) classification was implemented to calculate the auc of every network . these procedures were tested by monte carlo cross - validation ( mccv ) for 50 bootstraps . . the novel approach may be the basis of individual medical treatment in ces , serving as therapy targeting markers . one biological dataset , e - geod-58294 , was derived from the gene expression omnibus ( geo ) database ( http://www.ncbi.nlm.nih.gov/geo/ ) . the platform was a - affy-44  affymetrix genechip human genome u133 plus 2.0 , which was used to read the gene chip % h0 . the linear models for microarray data ( limma ) was then used to preprocess data . after quantile data normalization performed by robust multi - array average ( rma ) , 20 544 genes were obtained . in order to identify a group of pathways significantly enriched in ces with respect to controls , we collected 589 biological pathways including 5169 genes from the ipa tool ( http://www.ingenuity.com/ ) . after genes of expression profile were enriched in ipa pathways , we focused on 4929 genes . raw p - values were adjusted by false - discovery rate ( fdr ) procedure for multiple testing corrections . a total of 23 accumulated normal samples ( ans ) were used to identify ipa pathways as reference . individual normal sample gene expression was standardized with the mean and standard deviation ( sd ) . for genes of every ces sample , as quantile normalization was performed . average z equation was recently proved to be a biologically valid modification of pathway analysis methods for ipas . z=(z1 , z2 ,  , zn ) represents the expression state of a pathway where zi denotes the standardized expression value of i - th gene and the number of genes existing in the pathway is n. gene statistics of each gene from every ces sample : each ipa pathway statistics : zi represents the standardized gene level statistics of 1-i gene and the number of genes existing in the pathway is n. z values of every pathway in ces samples were gathered after significance testing . differentially expressed pathways were selected with z<0.05 . the discriminating score ( ds ) was computed to quantify pathway cross - talk in each sample for the pair of pathways x and y. ds was defined as where mx and sx represent mean and standard deviation of expression levels of genes in a pathway x and my and sy in a pathway y . ds score indicates the relationships between pairs of pathway , with a larger value indicating relatively higher difference of activity between pathways . ds of normal samples was standardized using the mean and sd as reference . z values of every pathway pair in ces samples z values of differentially expressed pathways and pathway pairs were used to construct individual networks with cytoscape version 3.2.0 . it randomly selected expression data in proportion 6:4 to form the training and testing set . then the process was repeated in 50 bootstraps , randomly generating new training and test partitions each time . the number of main networks appearing in the 50 bootstraps was counted by ranking all networks with their auc values . one biological dataset , e - geod-58294 , was derived from the gene expression omnibus ( geo ) database ( http://www.ncbi.nlm.nih.gov/geo/ ) . the platform was a - affy-44  affymetrix genechip human genome u133 plus 2.0 , which was used to read the gene chip % h0 . the linear models for microarray data ( limma ) was then used to preprocess data . after quantile data normalization performed by robust multi - array average ( rma ) , 20 544 genes were obtained . in order to identify a group of pathways significantly enriched in ces with respect to controls , we collected 589 biological pathways including 5169 genes from the ipa tool ( http://www.ingenuity.com/ ) . after genes of expression profile were enriched in ipa pathways , we focused on 4929 genes . raw p - values were adjusted by false - discovery rate ( fdr ) procedure for multiple testing corrections . a total of 23 accumulated normal samples ( ans ) were used to identify ipa pathways as reference . individual normal sample gene expression was standardized with the mean and standard deviation ( sd ) . for genes of every ces sample , as quantile normalization was performed . average z equation was recently proved to be a biologically valid modification of pathway analysis methods for ipas . z=(z1 , z2 ,  , zn ) represents the expression state of a pathway where zi denotes the standardized expression value of i - th gene and the number of genes existing in the pathway is n. gene statistics of each gene from every ces sample : each ipa pathway statistics : zi represents the standardized gene level statistics of 1-i gene and the number of genes existing in the pathway is n. z values of every pathway in ces samples were gathered after significance testing . the discriminating score ( ds ) was computed to quantify pathway cross - talk in each sample for the pair of pathways x and y. ds was defined as where mx and sx represent mean and standard deviation of expression levels of genes in a pathway x and my and sy in a pathway y . ds score indicates the relationships between pairs of pathway , with a larger value indicating relatively higher difference of activity between pathways . ds of normal samples was standardized using the mean and sd as reference . z values of every pathway pair in ces samples z values of differentially expressed pathways and pathway pairs were used to construct individual networks with cytoscape version 3.2.0 . it randomly selected expression data in proportion 6:4 to form the training and testing set . then the process was repeated in 50 bootstraps , randomly generating new training and test partitions each time . the number of main networks appearing in the 50 bootstraps was counted by ranking all networks with their auc values . in the present study we developed an integrated approach that was sufficient to construct individual networks comprising pathways cross - talk to quantify differences between ces and controls . rf classification was implemented to calculate auc of every network , which was tested by mccv for 50 bootstraps . we obtained a heatmap in which pink squares indicate pathway pairs for classification in the training dataset for that bootstrap ( the frequency > 6 ) . there were 4 pairs of pathways in 46 bootstraps : cholesterol biosynthesis i and cholesterol biosynthesis ii , cholesterol biosynthesis i and cholesterol biosynthesis iii , cholesterol biosynthesis ii and cholesterol biosynthesis iii , uracil degradation ii and thymine degradation . individual networks were ordered with respect to their auc and 28 networks with auc > 0.9 were found between ces and controls . among them , 3 networks with auc=1.0 had the best performance for classification of ces and normal samples for all 50 bootstraps . as shown in figure 2 , the best individual networks were in 4 , 10 , and 23 bootstraps . therefore , the 3 pathway networks were able to significantly identify ces versus controls , which showed as biomarkers in the regulation and development of ces . then we found there were 22 pairs of pathways that commonly appeared in 3 networks ( table 1 ) , which revealed that the pathway pairs were important in regulating ces . given the substantial difference in the activities of main networks between ces and controls , we examined its effectiveness in classifying ces and normal samples based on their profiles . in the best 3 networks , we focused on pathways that had multi - cross - talk with others . the msp - ron signaling pathway had the most cross - talk , which played an important interaction role in the best networks . a previous study has reported that msp - ron signaling is important for the invasive growth of many types of cancers and appeared to have potential as a therapeutic target . pathway analysis has become the first choice for extracting and explaining the underlying pathology for high - throughput molecular measurements . personalized identification of altered pathway pairs is important for understanding disease mechanisms and for the future application of custom therapeutic decisions . existing pathway analysis methods are not suitable for identifying the pathway aberrance that may occur in an individual sample . therefore , we employed the ipas to analyze the personalized identification of networks , taking advantage of a vast number of normal samples . a key innovation of the method is ipas using ans in ces . proved that the average z equation can efficiently reveal noticeable aberrance in expression profiles and clinical significance , which sufficed to confirm the best averaged validation rate and distinguish a known survival - relevant pathway statistically . furthermore , ans data is expected to be available in more fields of medicine along with rapid advances in high - throughput databases . ds obtained lightly more improvement than the euclidean distance as a metric to quantify pathway cross - talk . in recent years , different validation technologies have been generally used to evaluate performance of pathways and networks in medical regression analysis . the mccv pays attention to a notable part of the sample at a time during network building and validation with multi - repeats . compared with conventional validation tests for capturing the best predictor variables , mccv showed superior performance , resulting from a form of cross - validation based on vast combinations of data sets . interestingly , mccv has not been utilized in individual networks comprising pathways cross - talk in ces patients . in this study we developed an integrated approach to quantify differences between ces and controls with the mccv test , which suggests that mccv worked better , based on strong predictive ability . screened networks were efficient in distinguishing differences among individual ces samples , and can provide broader carcinogenic insight in personalized medicine . the final purpose of our approach was to detect the best network able to discriminate ces versus controls . we found that the 3 best networks were similar and had 22 common pairs of pathways . we tended to select network 10 to differentiate ces disease from normal samples , with the fewest pairs of pathways ( figure 2b ) . our novel approach identified 3 networks able to accurately classify ces and normal samples in individuals .