BACKGROUND
novel disease genes remain difficult to identify in most genetic diseases, and in particular, in highly polygenic disorders.
currently, not all genes have yet been detected even for those diseases whose molecular mechanisms are partially known  <cit> , for instance, breast cancer  <cit> .
breast cancer is a common cancer and a major cause of cancer death among females around the world, which makes up 23% of total cancer cases and 14% of cancer deaths  <cit> .
mining breast cancer genes is conducive to understand its pathogenic mechanism and search for effective treatments.
with rapid growth of disease-related genomic and functional data, computational approaches can be utilized to mine for new cancer genes  <cit> .
in the past two decades, a number of computational methods had been developed to mine potential disease related genes.
most of those methods rank candidate genes based on the idea that proteins similar to each other tend to cause similar or same diseases  <cit> .
they involve setting up a candidate gene set to be compared with a known disease gene set on their physical or functional attributes  <cit> .
on one hand, physical attribute-based methods include screening direct neighbors of known disease genes in the ppi network  <cit> , comparing shortest path length  <cit>  between candidate genes and known disease genes, clustering or graph partitioning to uncover disease modules in the interaction network  <cit> .
some approaches also used global network features to find genes similar with known disease genes  <cit> .
on the other hand, several methods rely on functional similarities between candidate and disease genes  <cit> , for example, some methods measured similarity between genes by their functional annotations  <cit>    <cit> ).
methods using other data sources had also been developed, such as gene expression  <cit> , biological pathways and sequence features  <cit> .
cancers such as breast cancer are complex and heterogeneous in nature, cancer-related genes often do not function in isolation but interact with one another  <cit> .
integrating multiple data types was found to be effective for gene mining in alleviating problems caused by incomplete information  <cit> .
for instance, endeavour  <cit>  is an online tool based on using multiple data sources.
it integrated candidate gene rankings from different data sources into a final ranking with the order statistic algorithm.
however, different data categories usually contain inherent noise or systematic errors  <cit> .
for instance, data from computational predictions will no doubt contain some amount of uncertainty.
experimental data obtained from different labs or experimental platforms can contain appreciable amount of noise.
noise in source data can push computed results away from their true values, lead to erroneous reporting.
a better method must be able to tolerate certain amount of noise, which makes the integration of different data sources more applicable to real-life scenarios.
despite the fact that some approaches can work with precision when presented with highly accurate data, few studies have shown that those methods worked robustly when faced with increasingly noisy data.
a number of papers had discussed the task of balancing noise and precision when using multiple data sources for cancer gene mining, however, hardly anyone had analyzed the noise problem quantitatively  <cit> .
it is important to calibrate how robust a method works with respect to noise, namely, how fast a method deteriorates when percentage of noise in source data goes up.
with that knowledge, users can then be confident about the method’s effectiveness when it is applied to real life data sets.
to tackle the data noise problem, a novel noise tolerant data fusion approach was proposed here for breast cancer gene mining , which integrated information from ppi network with gene expression data to rank genes based on their probabilities of being breast cancer related.
satisfactory results were obtained even when noise level was high.
to demonstrate advantages of the proposed method, its performance was compared with that of the random walk method  <cit> , which utilized a node’s global neighborhood in a network to rank genes.
random walk based methods had been shown to produce good performance  <cit>  in gene ranking.
results showed the proposed method exhibited better robustness when faced with increasingly noisy data, as compared to the random walk approach.
RESULTS
an effective data integration method was developed to mine breast cancer genes from four major data sources: protein-protein interactions, gene expression data, go annotations, and known breast cancer genes .
* for the known breast cancer gene set, three tools were used to perform the enrichment analyses of go terms in the bp sub-ontology: david , goeast , and gostats .
david and goeast are web tools; gostats is an r package from bioconductor.
cancer-hallmark go terms were extracted from table  <dig> of  <cit> .
after removing redundancy, a comprehensive human ppi network was constructed with data obtained from multiple interactions databases.
the resultant network contained a total of  <dig>  ppis with  <dig>  genes.
a noise tolerant method was designed to rank potential breast cancer genes.
rationale for data integration
a network-based score  and an expression-based score  were respectively derived for each gene, which were then integrated into a final score  by weighting them with a coefficient λ. a p-score was computed to represent performance of the proposed method when λ was changed from  <dig> to  <dig>  p-score was the average ranking of known breast cancer genes in top 10% of the final gene ranking list .
smaller p-score  meant better performance .
as shown in figure  <dig>  better performance was achieved when λ= <dig>  compared with λ= <dig> ; the best performance was obtained when λ= <dig> , which suggested that utilizing genes’ complex relations in the ppi network can help cancer gene mining tasks.
it also confirmed that multiple data sources can complement each other in ranking cancer genes.
final ranking results were listed in additional file 1: table s <dig>
evaluation of performance in ranking known cancer genes
known cancer genes were derived from the omim  and cgc  databases .
known breast cancer genes derived from omim was called omimset, those from cgc called cgcset.
omimset was used to train the proposed method, and cgcset was used for evaluation.
table  <dig> showed that the proposed method achieved better performance in ranking known breast cancer genes.
counting only genes ranked in top 10%, the proposed method achieved an average ranking of  <dig>  compared with  <dig> by random walk.
counting all  <dig> genes in cgcset, the proposed method achieved an average ranking of  <dig>  compared with  <dig> by random walk.
robustness with respect to realistic data noise
in general, a method based on multiple types of knowledge is more objective than those utilizing singular information; combining independent data sources can alleviate effects of biases inherent in single data types  <cit> .
most data sets, especially genome wide data, tend to contain appreciable amount of noise.
for instance, go is a powerful tool which provides a controlled vocabulary to describe biological functions on multiple levels  <cit> .
it was also widely used on cancer research .
however, not all go annotations are equally credible  <cit> .
as of october  <dig>  there were over  <dig> million go annotations for homo sapiens genes.
each go annotation includes an evidence code to indicate how the annotation was inferred.
all evidence codes  are manually assigned.
they can be divided into four categories: experimental , computational analysis , author statements , and curatorial statements .
for homo sapiens,  <dig> evidence codes were used to describe go annotations  <cit> .
the vast majority of go annotations were found to be computationally derived and not manually curated  .
go annotations therefore contain appreciable amount of noise within themselves.
approaches that exhibit robust performance with regard to noise are needed if they are to prove useful in cancer gene hunting endeavours.
nevertheless, as mentioned before, few projects had specifically analysed data noise effects quantitatively.
a network based noise tolerant method was proposed here to mine breast cancer genes.
its performance was compared with that of the well performing random walk approach by five-fold cross-validation.
the results confirmed the proposed method’s robust performance with respect to data noise.
the set of known breast cancer genes  was enlarged by including genes sharing go annotations with those in the knownset.
the enlarged set was called the goset , which was adopted as a noisy set of likely breast cancer genes.
the goset was utilized to check an algorithm’s robustness with respect to data noise.
data were sampled from the goset, and combined with the knowset to generate a noisy set of training data.
this way of synthesizing noisy data set is unique in that it doesn’t simply using random data as noise, which is too artificial.
the goset contains enriched but still imperfect data, which can better mimic data noise in real life scenarios.
an algorithm’s ability to retain its performance was checked when fraction of noisy data in the training set went up.
the proposed method did not work quite as well as random walk approach when input data was 100% accurate, however, when noise level in input data increased, we observed the following phenomena .
<dig>  random walk approach exhibited a sharp decrease in its performance, while our method was able to maintain a relatively stable performance.
<dig>  when noise increased to a large extent, the proposed method was able to perform about twice as well as random walk approach did.
it can thus be stated that the proposed method was more robust with respect to noise in input data, compared to the state-of-art random walk based approach.
the results also confirmed the power of data integration, which was able to let different data sets complementing each other  <cit> .
robustness with respect to completely random noise
goset tried to simulate realistic data noise.
however, it might be suspected that goset was biased toward the proposed method in one way or another.
to make sure the comparison between the proposed method and the random walk approach was not done unfairly, randomly picked genes were added to the knownset , and performances of the proposed method and random walk approach were then compared.
figure  <dig> again showed that random walk approach showed linear performance decline when noise increased in the source data, while the proposed method was able to maintain stable performance.
CONCLUSIONS
cancers are highly complex processes, the majority of cancer genes are yet to be mapped.
currently available data  are too limited to be really effective for cancer gene searching purpose.
broadening the scope of input data  should enable better use of available data to mine for new cancer genes.
approaches that work robustly against data noise are needed.
a novel noise tolerant breast cancer gene mining method was presented here, which integrated a comprehensive ppi network, gene expression data, prior knowledge of breast cancer and go annotations to rank potential breast cancer genes.
from each data source, a ranked list for each candidate gene was computed, and they were then combined into a final ranking order.
influence of data noise was quantitatively evaluated.
random walk approach performed better than the proposed method using 100% accurate input data .
however, the proposed method showed much greater noise tolerance.
to our best knowledge, this is the first effort to quantitatively analyse noise tolerance between different cancer gene mining methods.
the framework of the proposed mining method and the quantitative way of appraising noise effects are flexible enough to be useful for other data sources, and hopefully, lead to more discussions on data noise issue for different computational methods in cancer gene mining field.

 novel disease genes remain difficult to identify in most genetic diseases, and in particular, in highly polygenic disorders. currently, not all genes have yet been detected even for those diseases whose molecular mechanisms are partially known, for instance, breast cancer. breast cancer is a common cancer and a major cause of cancer death among females around the world, which makes up 23% of total cancer cases and 14% of cancer deaths. mining breast cancer genes is conducive to understand its pathogenic mechanism and search for effective treatments. with rapid growth of disease-related genomic and functional data, computational approaches can be utilized to mine for new cancer genes. in the past two decades, a number of computational methods had been developed to mine potential disease related genes. most of those methods rank candidate genes based on the idea that proteins similar to each other tend to cause similar or same diseases. they involve setting up a candidate gene set to be compared with a known disease gene set on their physical or functional attributes. on one hand, physical attribute-based methods include screening direct neighbors of known disease genes in the ppi network, comparing shortest path length between candidate genes and known disease genes, clustering or graph partitioning to uncover disease modules in the interaction network. some approaches also used global network features to find genes similar with known disease genes. on the other hand, several methods rely on functional similarities between candidate and disease genes, for example, some methods measured similarity between genes by their functional annotations ). methods using other data sources had also been developed, such as gene expression, biological pathways and sequence features. cancers such as breast cancer are complex and heterogeneous in nature, cancer-related genes often do not function in isolation but interact with one another. integrating multiple data types was found to be effective for gene mining in alleviating problems caused by incomplete information. for instance, endeavour is an online tool based on using multiple data sources. it integrated candidate gene rankings from different data sources into a final ranking with the order statistic algorithm. however, different data categories usually contain inherent noise or systematic errors. for instance, data from computational predictions will no doubt contain some amount of uncertainty. experimental data obtained from different labs or experimental platforms can contain appreciable amount of noise. noise in source data can push computed results away from their true values, lead to erroneous reporting. a better method must be able to tolerate certain amount of noise, which makes the integration of different data sources more applicable to real-life scenarios. despite the fact that some approaches can work with precision when presented with highly accurate data, few studies have shown that those methods worked robustly when faced with increasingly noisy data. a number of papers had discussed the task of balancing noise and precision when using multiple data sources for cancer gene mining, however, hardly anyone had analyzed the noise problem quantitatively. it is important to calibrate how robust a method works with respect to noise, namely, how fast a method deteriorates when percentage of noise in source data goes up. with that knowledge, users can then be confident about the method’s effectiveness when it is applied to real life data sets. to tackle the data noise problem, a novel noise tolerant data fusion approach was proposed here for breast cancer gene mining, which integrated information from ppi network with gene expression data to rank genes based on their probabilities of being breast cancer related. satisfactory results were obtained even when noise level was high. to demonstrate advantages of the proposed method, its performance was compared with that of the random walk method, which utilized a node’s global neighborhood in a network to rank genes. random walk based methods had been shown to produce good performance in gene ranking. results showed the proposed method exhibited better robustness when faced with increasingly noisy data, as compared to the random walk approach.  an effective data integration method was developed to mine breast cancer genes from four major data sources: protein-protein interactions, gene expression data, go annotations, and known breast cancer genes. * for the known breast cancer gene set, three tools were used to perform the enrichment analyses of go terms in the bp sub-ontology: david, goeast, and gostats. david and goeast are web tools; gostats is an r package from bioconductor. cancer-hallmark go terms were extracted from table  of. after removing redundancy, a comprehensive human ppi network was constructed with data obtained from multiple interactions databases. the resultant network contained a total of ppis with genes. a noise tolerant method was designed to rank potential breast cancer genes. rationale for data integration a network-based score and an expression-based score were respectively derived for each gene, which were then integrated into a final score by weighting them with a coefficient λ. a p-score was computed to represent performance of the proposed method when λ was changed from to p-score was the average ranking of known breast cancer genes in top 10% of the final gene ranking list. smaller p-score meant better performance. as shown in figure  better performance was achieved when λ= compared with λ=; the best performance was obtained when λ=, which suggested that utilizing genes’ complex relations in the ppi network can help cancer gene mining tasks. it also confirmed that multiple data sources can complement each other in ranking cancer genes. final ranking results were listed in additional file 1: table s evaluation of performance in ranking known cancer genes known cancer genes were derived from the omim and cgc databases. known breast cancer genes derived from omim was called omimset, those from cgc called cgcset. omimset was used to train the proposed method, and cgcset was used for evaluation. table  showed that the proposed method achieved better performance in ranking known breast cancer genes. counting only genes ranked in top 10%, the proposed method achieved an average ranking of compared with by random walk. counting all genes in cgcset, the proposed method achieved an average ranking of compared with by random walk. robustness with respect to realistic data noise in general, a method based on multiple types of knowledge is more objective than those utilizing singular information; combining independent data sources can alleviate effects of biases inherent in single data types. most data sets, especially genome wide data, tend to contain appreciable amount of noise. for instance, go is a powerful tool which provides a controlled vocabulary to describe biological functions on multiple levels. it was also widely used on cancer research. however, not all go annotations are equally credible. as of october there were over million go annotations for homo sapiens genes. each go annotation includes an evidence code to indicate how the annotation was inferred. all evidence codes are manually assigned. they can be divided into four categories: experimental, computational analysis, author statements, and curatorial statements. for homo sapiens, evidence codes were used to describe go annotations. the vast majority of go annotations were found to be computationally derived and not manually curated. go annotations therefore contain appreciable amount of noise within themselves. approaches that exhibit robust performance with regard to noise are needed if they are to prove useful in cancer gene hunting endeavours. nevertheless, as mentioned before, few projects had specifically analysed data noise effects quantitatively. a network based noise tolerant method was proposed here to mine breast cancer genes. its performance was compared with that of the well performing random walk approach by five-fold cross-validation. the results confirmed the proposed method’s robust performance with respect to data noise. the set of known breast cancer genes was enlarged by including genes sharing go annotations with those in the knownset. the enlarged set was called the goset, which was adopted as a noisy set of likely breast cancer genes. the goset was utilized to check an algorithm’s robustness with respect to data noise. data were sampled from the goset, and combined with the knowset to generate a noisy set of training data. this way of synthesizing noisy data set is unique in that it doesn’t simply using random data as noise, which is too artificial. the goset contains enriched but still imperfect data, which can better mimic data noise in real life scenarios. an algorithm’s ability to retain its performance was checked when fraction of noisy data in the training set went up. the proposed method did not work quite as well as random walk approach when input data was 100% accurate, however, when noise level in input data increased, we observed the following phenomena. random walk approach exhibited a sharp decrease in its performance, while our method was able to maintain a relatively stable performance. when noise increased to a large extent, the proposed method was able to perform about twice as well as random walk approach did. it can thus be stated that the proposed method was more robust with respect to noise in input data, compared to the state-of-art random walk based approach. the results also confirmed the power of data integration, which was able to let different data sets complementing each other. robustness with respect to completely random noise goset tried to simulate realistic data noise. however, it might be suspected that goset was biased toward the proposed method in one way or another. to make sure the comparison between the proposed method and the random walk approach was not done unfairly, randomly picked genes were added to the knownset, and performances of the proposed method and random walk approach were then compared. figure  again showed that random walk approach showed linear performance decline when noise increased in the source data, while the proposed method was able to maintain stable performance.  cancers are highly complex processes, the majority of cancer genes are yet to be mapped. currently available data are too limited to be really effective for cancer gene searching purpose. broadening the scope of input data should enable better use of available data to mine for new cancer genes. approaches that work robustly against data noise are needed. a novel noise tolerant breast cancer gene mining method was presented here, which integrated a comprehensive ppi network, gene expression data, prior knowledge of breast cancer and go annotations to rank potential breast cancer genes. from each data source, a ranked list for each candidate gene was computed, and they were then combined into a final ranking order. influence of data noise was quantitatively evaluated. random walk approach performed better than the proposed method using 100% accurate input data. however, the proposed method showed much greater noise tolerance. to our best knowledge, this is the first effort to quantitatively analyse noise tolerance between different cancer gene mining methods. the framework of the proposed mining method and the quantitative way of appraising noise effects are flexible enough to be useful for other data sources, and hopefully, lead to more discussions on data noise issue for different computational methods in cancer gene mining field. 
performance of the proposed method was quantitatively evaluated by comparing it with the more established random walk approach. results showed that the proposed method exhibited better performance in ranking known breast cancer genes and higher robustness against data noise than the random walk approach. when noise started to increase, the proposed method was able to maintained relatively stable performance, while the random walk approach showed drastic performance decline; when noise increased to a large extent, the proposed method was still able to achieve better performance than random walk did. compared to the well established random walk approach, it showed better performance in correctly ranking cancer genes and worked robustly with respect to noise within source data. mining novel breast cancer genes is an important task in breast cancer research. many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. a novel noise tolerant approach was proposed to rank candidate breast cancer genes using noisy source data within the framework of a comprehensive human protein-protein interaction network. a novel noise tolerant method was proposed to mine breast cancer genes. it is hoped that the current work can lead to more discussions about influence of data noise on different computational methods for mining disease genes. however, the vast majority of go annotations were computationally derived, thus not completely accurate. gene ontology annotations were often utilized in cancer gene mining works. to the best of our knowledge, it’s the first such effort to quantitatively analyze noise tolerance between different breast cancer gene mining methods. a set of genes annotated with breast cancer enriched go terms was adopted here as a set of source data with realistic noise. the sorted gene list can be valuable for breast cancer research.
 mining novel breast cancer genes is an important task in breast cancer research. many approaches prioritize candidate genes based on their similarity to known cancer genes, usually by integrating multiple data sources. however, different types of data often contain varying degrees of noise. for effective data integration, it’s important to design methods that work robustly with respect to noise.  gene ontology annotations were often utilized in cancer gene mining works. however, the vast majority of go annotations were computationally derived, thus not completely accurate. a set of genes annotated with breast cancer enriched go terms was adopted here as a set of source data with realistic noise. a novel noise tolerant approach was proposed to rank candidate breast cancer genes using noisy source data within the framework of a comprehensive human protein-protein interaction network. performance of the proposed method was quantitatively evaluated by comparing it with the more established random walk approach. results showed that the proposed method exhibited better performance in ranking known breast cancer genes and higher robustness against data noise than the random walk approach. when noise started to increase, the proposed method was able to maintained relatively stable performance, while the random walk approach showed drastic performance decline; when noise increased to a large extent, the proposed method was still able to achieve better performance than random walk did.  a novel noise tolerant method was proposed to mine breast cancer genes. compared to the well established random walk approach, it showed better performance in correctly ranking cancer genes and worked robustly with respect to noise within source data. to the best of our knowledge, it’s the first such effort to quantitatively analyze noise tolerance between different breast cancer gene mining methods. the sorted gene list can be valuable for breast cancer research. the proposed quantitative noise analysis method may also prove useful for other data integration efforts. it is hoped that the current work can lead to more discussions about influence of data noise on different computational methods for mining disease genes. networkbreast cancerdata noisenoise tolerance 