BACKGROUND
discovering closely related diseases could be helpful in revealing their common pathophysiology  <cit> .
it may also be useful for identifying novel drug indications  <cit> , as similar diseases may have the same or similar therapeutic targets, which suggests they could be treated with the same or similar drugs.
there has been a growing interest in quantitatively measuring similarities between diseases .
phenotypic similarity plays an important role in a number of biological and biomedical applications  <cit> .
during the past years, based on the human phenotype ontology   <cit> , researchers had designed several methods to find related diseases and predict disease-causing genes, such as phenomizer  <cit> , exomiser  <cit>  and phenix  <cit> .
the hpo provides a controlled and standardized vocabulary of phenotypic abnormalities that characterize human diseases.
phenotype similarity also, becomes the most common way to define classification rules for diseases.
the classification of disease terms in medical subject headings   <cit>  and disease ontology   <cit>  are taking this approach.
to quantify disease similarity, several semantic-based methods had thus been proposed based on hpo, mesh or do, such as resnik  <cit> , lin  <cit>  and wang  <cit> .
resnik’s method measures disease similarity based on information content  of the most informative common ancestor  between two terms.
besides ic of mica, lin’s method also considers the ic of the two compared diseases  <cit> .
wang et al.’s method  <cit>  computes similarity of a disease pair by considering the contribution of all common ancestors in the ontology.
it had been successfully applied to compute similarity between mesh  <cit>  terms.
all of those semantic-based methods exploited disease associations based on ontologies and/or gene annotations.
they did not, however, consider the functional associations between disease-related gene sets.
the bog  method was thus designed by mathur and dinakarpandian  <cit> , which calculates disease similarity by exploiting the co-occurrence of disease-related genes.
mathur et al.
<cit>  also devised a process-similarity based  method.
instead of defining disease similarity as a function of genes, psb computes disease similarity based on gene ontology   <cit>  biological process terms associated with those genes.
psb achieved a better performance than bog  <cit> .
functional associations between genes involve not only go terms  <cit> , but also co-expression  <cit> , protein-protein interaction  <cit> , etc.
cheng et al.
recently presented the method funsim  <cit> , which measures disease similarity using a weighted human protein interaction network.
the first neighbors of disease-related genes in the protein network were taken into account.
funsim further improved the results of psb  <cit> .
although a number of methods for computing disease similarity had been developed, no method had been proposed to take advantage of the entire protein interaction network, beyond using only the first neighbors.
a network-based method  is proposed which takes advantage of the entire interaction network.
the effectiveness of different data sources were also evaluated, including gene-disease associations and protein-protein interactions.
most of the previously developed methods were based on disease-related genes.
however, many diseases still have very few or no associated genes.
relying entirely on disease-related genes greatly limits the utility of those methods.
to overcome the limitation, a new semantic-based similarity measure  is developed to compute disease similarity based on the medline database.
medsim and netsim were eventually integrated into mednetsim to further improve computing performance.
methods
diseases and gene-disease association databases
the disease terms in do were chosen as the vocabulary for describing diseases.
do database is a biomedical resource of disease concepts with stable identifiers organized by disease etiology  <cit> .
it contains  <dig>  non-obsolete disease terms and  <dig>  ‘is_a’ relationships among diseases.
the non-obsolete disease terms was used as the disease vocabulary.
each disease in do has a unique identifier, called doid.
sidd  <cit>  and disgenet  <cit>  were adopted as two disease-gene association databases .
sidd integrated five disease-related gene databases: generif  <cit> , online mendelian inheritance in man   <cit> , comparative toxicogenomics database   <cit> , genetic association database   <cit> , and splicedisease  <cit> .
in total, sidd contains  <dig>  diseases and  <dig>  gene-disease associations .
the disgenet  <cit>  database integrated human gene-disease associations from various expert curated databases and text-mining derived associations including mendelian, complex and environmental diseases.
compared to sidd, disgenet had more lower reliability disease-gene associations based on literature mining, i.e., lhgdn  <cit>  and befree data  <cit> .
disgenet contains  <dig>  diseases and  <dig>  gene-disease associations.
umls id  was used as the unique identifier for each disease in disgenet.
we mapped umls id to doid, which produced  <dig>  disease terms and  <dig>  gene-disease associations .
almost every disease term in disgenet has more associated genes than that in sidd.
all source data were downloaded until april  <dig>   <dig> fig  <dig> gene-disease association databases.
a : the number of diseases, b: the number of associations between genes and diseases
protein interaction datasets
two protein interaction datasets were used .
one is hppin, built in house, which integrated four existing protein interaction databases, i.e., biogrid  <cit> , hprd  <cit> , intact  <cit> , and homomint  <cit> .
protein identifiers were mapped to the genes coding for the proteins, and redundant interactions were removed.
the acquired protein interaction network covered  <dig>  human genes and  <dig>  interactions .
the other is humannet  <cit> , which is a genome-scale functional network for human genes.
to build humannet,  <dig> diverse functional genomics and proteomics datasets were evaluated for their tendencies to link human genes in the same biological processes.
pairwise gene linkages derived from the individual datasets were then integrated into a comprehensive humannet  <cit> .
humannet contains  <dig>  functional linkages among  <dig>  human genes .
unlike hppin which mainly focuses on experimentally verified protein interactions, humannet was constructed based on the functional probability that two genes belonged to the same biological processes.
the two protein interaction datasets have  <dig>  genes and  <dig>  interactions in common .
additionally, different proportions of hppin  were randomly sampled  <dig> times and used as the protein interaction datasets to evaulate the impact of data volume on the proposed method.fig  <dig> protein interaction datasets.
a: the number of genes, b: the number of interactions between genes, *: the common protein-protein interactions
medline-based disease similarity
biomedical literature contains rich and diverse information, such as disease symptoms, pathogenesis, therapeutic drugs, and so on.
features representing diseases were generated through mining the biomedical literature corpus; the features were then utilized to compute disease similarity .
medsim was not limited to use only one aspect of disease information , but took advantages of all relevant information that had already been archived in the literature.fig  <dig> overview of medsim.
do: human disease ontology database; umls: unified medical language system
disease corpus
the text corpus contains all medline abstracts published up to year  <dig>  the non-obsolete disease terms in do were used as the disease vocabulary.
each disease term was mapped to unified medical language system   <cit>  so that its synonyms could be retrieved.
synonyms were taken directly from do for diseases that could not be mapped to umls.
every disease term and its synonyms were then used as keywords to perform keyword-based queries into medline to retrieve abstracts related to that disease.
to limit computational cost, only the top  <dig> most relevant abstracts were selected to construct the bag-of-words model for diseases.
the relevance of an abstract to a disease was defined in eq.
<dig>   <dig> rabstract=∑wwdf×wof
where wdf and wof represent document frequency and occurrence frequency of a word x, respectively.
document frequency wdf is the proportion of abstracts that contain word x. wdf represents the relevance of word x to a disease.
occurrence frequency wof represents the number of times word x occurs in an abstract, measuring the importance of word x in a specific abstract.
for a specific disease, w is defined as the set of nouns  which appeared in abstracts when wdf is greater than  <dig> .
larger rabstract means that an abstract is more closely related to the disease.
some diseases were not yet broadly studied, so their number of retrieved abstracts can be less than  <dig>  for those cases, all retrieved abstracts were used.
for each disease, the selected most relevant abstracts were merged into one combined document.
at the end of preprocessing, every disease was associated with one document.
these documents together made up the disease corpus.
constructing the bag-of-words model and computing medsim
the disease corpus was tokenized to obtain word vocabulary, using python package nltk  to remove non-alphabetic words and reduce inflected/derived words to their stem.
overly common  or rare  words were removed, as those words could not provide meaningful information.
each disease was then represented by a word vector, whose dimensionality is the size of the word vocabulary.
each dimension was assigned a weight  based on term frequency  and inverse document frequency  values.
tf is the number of times a word appears in a document.
idf represents the inverse of the number of documents containing the word.
tf-idf assigns larger weights to words that appeared more often in a document but only in a small percentage of all documents, as those words are important and informative for that document.
with diseases represented as tf-idf weighted vectors, the medsim of two diseases was measured by calculating the cosine similarity of the two vectors.
python package scikit-learn  <cit>  was used to perform the computation.
network-based disease similarity
previously published methods weren’t designed to utilize the entire protein interaction network.
they instead focused only on the disease-related genes or their first neighbors in the network.
to take full advantage of the entire protein interaction network, random walk with restart   <cit>   was used to measure functional relevance  between a gene g and a gene set g, which is described in eq.
<dig>   <dig> frg={prwrg∈proteininteractionnetwork1g∉proteininteractionnetworkandg∈g0g∉proteininteractionnetworkandg∉g)
where gene set g was defined to be the seed genes, that is, the known set of genes associated with a disease.
the initial probability of each seed genes was set to  <dig> .
prwr represents the acquired steady-state probability of gene g after running rwr in the whole protein interaction network.
a larger probability ) will be assigned to gene g when it sits more closely to the gene set g in the network according to eq.
<dig>  which means that gene g are more functionally related with gene set g.
suppose that g1 = {g <dig> g <dig> …} and g2 = {g <dig> g <dig> …} are the seed gene sets for disease d <dig> and d <dig>  respectively.
then, the netsim of d <dig> and d <dig> is defined in eq.
<dig>   <dig> nesimg1g2=∑1≤i≤leng1frg2g1i+∑1≤j≤leng2frg1g2jleng1+leng <dig> g1i∈g <dig> g2j∈g <dig>
where len and len are the number of genes in g <dig> and g <dig>  respectively.
the numerator is the sum of functional relevance of g1i to g <dig> and g2j to g <dig>  a higher netsim value represents closer connection between g <dig> and g <dig>  which suggests closer ties between diseases d <dig> and d <dig>
medsim and netsim is combined into mednetsim, which is defined in eq.
<dig>   <dig> mednetsimd1d2=medsimd1d2×netsimg1g <dig>
where d <dig> and d <dig> are two diseases in do, g <dig> and g <dig> are the seed gene sets for d <dig> and d <dig>  respectively.
performance evaluation
similarities of disease pairs in the benchmark set and the random set were calculated and ranked in descending order, receiver operating characteristic   <cit>  curves were then drawn to evaluate and quantify the predictive power of the proposed methods.
a roc curve is a plot of the true positive rate of a classifier as a function of the false positive rate.
the area under the roc curve  is used as a quantitative measure of a classifier’s quality  <cit> .
disease pairs in the benchmark set and the random set are defined as positives and negatives, respectively.
true positives are the disease pairs in the benchmark set that are correctly predicted by a classifier, and false positives are those disease pairs from the random set that are predicted to be positives but not found in the benchmark set.
more percentage of disease pairs in the benchmark set receiving higher rankings means better auc values.
the benchmark set was taken from reference  <cit> .
it had  <dig> diseases and  <dig> disease pairs  with high similarity derived from two manually checked datasets by suthram et al.
<cit>  and pakhomov et al.
<cit> .
cancers were omitted.
the benchmark set contains disease pairs that are expected to be related to each other, such as alzheimer’s disease  and schizophrenia , diabetes mellitus  and obesity .
it also includes some pairs that are not apparently related, but were found to be correlated by various evidences, such as asthma  and diabetes mellitus, malaria  and anemia .
<dig> disease pairs were randomly selected from do to generate a random set, with disease pairs from the benchmark set removed from the generated random set.
to get an average auc of the proposed methods, the above experiment was iterated  <dig> times by calculating similarities of disease pairs in the benchmark set and  <dig> random sets.
medsim was compared with other semantic-based methods including resnik  <cit> , lin  <cit>  and wang  <cit> , based on hpo and do, respectively.
for each disease, the associated hpo annotations were acquired from  <cit> , which covered disease-phenotype associations for over  <dig> common, rare, infectious and mendelian diseases through text-mining approach.
the hpo-based disease similarities were defined by calculating the semantic similarity of their associated hpo phenotypes.
for two diseases , the hpo-based similarity of d <dig> to d <dig> is defined as follows:  <dig> hpo_simd1→d2=avg∑s∈d1maxt∈d2semsimst
where s and t are the annotated phenotypes of d <dig> and d <dig>  respectively.
semsim() is one of the methods applied to compute the semantic similarity of two phenotype terms, including resnik, lin and wang.
eq.
<dig>  for each phenotype term of d <dig>  found the “best match” among the phenotype terms annotated to d <dig>  and the average overall phenotype terms was calculated.
note that this similarity is asymmetric, i.e., hpo_sim is not always equal to hpo_sim.
therefore, we used a symmetric hpo-based similarity, which is defined in eq.
6:  <dig> hpo_simd1d2=12hpo_simd1→d2+12hpo_simd2→d <dig>
the do-based disease similarities were defined as the directly semantic similarity of two disease terms in do, where the above mentioned three semantic-base methods  were applied, too.
netsim was also compared with other function-based methods including bog  <cit> , psb  <cit>  and funsim  <cit> .
parameters of the aforementioned methods were set to values used in the original paper.
constructing disease similarity network
disease terms from do were used as nodes in the similarity network between diseases .
we computed the pair-wise similarity for a total of  <dig>  diseases  by the proposed method mednetsim.
if the similarity of a disease pair was ranked in the top  <dig>  %, an undirected weighted edge between the disease pair was drawn.
the network was visualized with the force-directed layout algorithm of cytoscape  <cit>  and colored according to top-level do categories.
RESULTS
utilizing the entire network benefits disease similarity computation
similarities of disease pairs in the benchmark set and a random set were calculated by netsim and other function-based methods.
as shown in fig.
4a, the bog method, with an auc of  <dig>  %, had the worst performance among function-based methods.
linking genes based on the go biological process ontology  <cit> , psb method had significantly improved performance, achieving an auc of  <dig>  %.
considering nearest neighbors of disease-related genes in protein interaction network, funsim improved its auc to  <dig>  %.
the proposed method, netsim, which utilized the entire protein interaction network, further improved its auc to  <dig>  %.
the results show that utilizing the entire network can increase computing performance for disease similarity calculation.
integrating medsim  and netsim, the mednetsim achieved the highest auc among all function-based methods, improving its auc to  <dig>  %.
the performance improvement indicates that integration of medline and protein interaction network can be an effective way to compute disease similarities.
to check the stability of netsim and mednetsim, the above computation was repeated  <dig> times by calculating similarities using  <dig> randomly generated disease pair sets.
fig 4b shows the average auc of bog , psb , funsim , netsim  and mednetsim , which is consistent with fig.
4a.fig  <dig> performance of function-based methods.
a roc curves for the benchmark set and a random set.
b average auc for the benchmark set and  <dig> random sets
the mednetsim similarity values of all disease pairs were computed, and a distribution of  <dig> , <dig> similarity values  was acquired.
the ranking of a similarity value in the distribution was used to compute its corresponding p-value.
if the mednetsim similarity value of a disease pair is in the highest-ranking 5 % of the distribution , the two diseases are considered related.
to evaluate the ability of mednetsim in discriminating positive and negative cases, the p-values of similarities of disease pairs in the benchmark set and a random set were calculated .
for the benchmark set,  <dig> disease pairs were recognized as highly related diseases correctly and  <dig> disease pairs did not show a significant p-values .
the false negatives can be divided into two groups.
the first group had a non-significant p-value of medsim similarity, but a significant p-value of netsim similarity, e.g., polycystic ovary syndrome  & myocardial infarction , malaria  & epilepsy syndrome  .
the missed calling of being positives for those disease pairs was mainly due to the very bad results of medsim.
that is to say, the research literature contains less information about their relatedness, therefore dragging down the performance of mednetsim.
for those disease pairs, netsim may be a better choice.
in the second group, both medsim and netsim similarities did not show significant p-values.
a representative disease of the second group was lipid storage disease .
<dig> out of the  <dig> disease pairs between lipid storage disease and other diseases in the benchmark set were incorrectly identified, e.g., lipid storage disease & obesity , lipid storage disease & diabetes mellitus  .
the number of associated genes of obesity and diabetes mellitus was  <dig>  and  <dig> , respectively.
lipid storage disease only had  <dig> associated genes.
out of the  <dig> associated genes,  <dig> and  <dig> genes were shared by obesity and diabetes mellitus, respectively.
although more than 1/ <dig> associated genes of lipid storage disease appeared in obesity and diabetes mellitus, they still got a bad netsim results.
that is because obesity and diabetes mellitus had a much bigger number of associated genes than lipid storage disease.
this indicates that netsim performs less well when two diseases have a large difference in the number of disease-associated genes.
for the random set,  <dig> out of  <dig> disease pairs were recognized as related diseases .
more than half of the  <dig> disease pairs were cancer related diseases, e.g., penile neoplasm  & cecum cancer , pancreatic cancer  & tubular adenocarcinoma  .
as cancer diseases were omitted in selecting benchmark set, it is not surprising that so many disease pairs related to cancers are detected as false positives.
the relatedness of diseases belonging to different top-level do categories was also identified, e.g., essential hypertension  & hyperthyroidism .
recently, emokpae et al.
had pointed out that hyperthyroidism was the most common thyroid disorder observed in patients with essential hypertension  <cit> .
it indicates that our method can recognize related diseases which apparently seem unrelated.
in addition, the relationship of impulse control disorder  & narcissistic personality disorder  was also detected .
the two disease are both in the “disease of mental health”  category, but there is no report on their relatedness.
therefore, mednetsim can also discover new unknown relatedness among diseases.table  <dig> examples of false negatives and false positives with p-values from mednetsim
medsim can be a useful supplement to function-based methods
roc curves of medsim and other semantic-based methods based on hpo and do, respectively, were also generated .
for the methods based on hpo, lin’s method  had the worst performance with an auc of only  <dig>  %, and wang et al.’s method  acquired the best performance among the three methods.
as hpo was replaced by do to calculate disease similarity, resnik’s method  became the worst method, and wang et al.’s method still had the best performance with an auc of  <dig>  %.
overall, performances of hpo-based methods are similar to do-based methods.
however, compared to computing disease similarity based on ontologies, the proposed medsim had a significantly better performance than those methods.
medsim achieved an auc of  <dig>  %, which is even slightly better than the function-based method bog.
figure 5b shows the average auc for all semantic-based methods.
the result is consistent with fig.
5a.fig  <dig> performance of semantic-based methods.
a roc curves for the experimental results on the benchmark set and a random set.
b average auc for the benchmark set and  <dig> random sets.
hpo_res, hpo_lin and hpo_wang denoted disease similarities computation by using resnik, lin and wang based on hpo, respectively
two reasons may explain why medsim achieved the best performance among semantic-based methods.
on the one hand, previous methods suffered from the incompleteness of ontologies and the lack of coverage of gene-disease or phenotype-disease association data.
for example, only one-third of do diseases have associated genes .
hpo is widely used in the rare disease community  <cit> .
however, the infrastructure of phenotype data for common and infectious diseases  <cit>  is still developing.
on the other hand, medsim considered much richer and more diverse information included in literature, not only disease-related genes, but also disease symptoms, pathogenesis, therapeutic drugs, and so on.
medsim requires only biomedical literature, no requirement to know disease-associated gene sets and ontologies.
it thus has much broader applicability than previously published methods, especially in the case of no sufficient gene-disease association data.
the impact of different data sources
gene-disease association databases
the effectiveness of different gene-disease association data was evaluated.
disgenet was used as a replacement for sidd.
compared to sidd, disgenet has much more lower reliability associations based on literature mining.
its disease-gene associations are nearly two times of those in sidd, with only 34 % more disease terms .
using disgenet as gene-disease association data source, the auc of netsim  grew to  <dig>  % , which is even better than mednetsim  that fused medsim and netsim.
integration of medsim and netsim_dgn  produced an auc of  <dig>  % .
fig.
6b shows the average auc of netsim_dgn  and mednetsim_dgn , which is consistent with fig.
6a too.
the above observations show that a richer gene-disease association data, even with a lower reliability, is favorable for discovering relatedness between diseases.fig  <dig> the impact of different data sources.
a roc curves for the experimental results on the benchmark set and a random set.
b average auc for the benchmark set and  <dig> random sets
protein interaction datasets
to gauge the impact of different interaction datasets on computing performance, humannet database was used as the protein interaction network, substituting hppin.
the number of protein nodes in humannet and hppin do not differ greatly, but the number of interactions in humannet is more than three times that of hppin .
however, the performance of netsim while using humannet  did not improve at all compared to using hppin, with both achieving an auc of  <dig>  % .
furthermore, the common interaction pairs of hppin and humannet  were also applied as the protein interaction network to evaluate the performance of netsim .
although comppi had a much smaller dataset than hppin or humannet, netsim_comppi achieved the same performance as netsim and netsim_hn, with an auc of  <dig>  % too.
the average auc of netsim_hn and netsim_comppi  also showed the same results.
additionally, the average auc of netsim with different proportions of hppin were also computed.
as shown in fig.
<dig>  the average auc increased rapidly at the beginning, it then leveled off and did not grow as fast once the sampling rate hit 60 %.
the average auc plateaued at a sampling rate of 80 %.
the above results indicate that merely using more protein interaction data does not lead to improved performance of netsim.
it might partially explain why using humannet, which has more than three times protein interaction data than hppin, did not improve the performance of netsim.fig  <dig> the average auc of netsim with different proportion of hppin sampled
percentage of interaction pairs sharing go annotation was analyzed for humannet, hppin and their common protein interactions  .
for the entire go annotation and its three categories , the percentage of pairs sharing annotation in hppin was higher than that in humannet, suggesting hppin has a higher data quality than humannet.
the fact that humannet did not achieve improved performance for netsim may partially be due to humannet’s lower data quality than that of hppin.
in addition, whether the entire go or its three categories, comppi had the highest percentage of protein pairs sharing annotation in the three datasets, indicating that comppi has the best data quality.
the highest data quality of comppi may be responsible for it acquiring same performance as that of hppin or humannet.
all those results suggest that the quality of protein interaction data is more important than its volume for the computation of disease similarity.table  <dig> percentage of interaction pairs sharing go annotation
comppia
go gene ontology, go_bp biological process, go_cc cellular component, go_mf molecular function
athe common protein-protein interactions between humannet and hppin
disease similarity network
as shown in fig.
<dig>  a disease similarity network  was generatedty based on mednetsim from the top-ranking  <dig>  % of pair-wise similarity values among  <dig>  diseases in do.
<dig>  of the  <dig>  diseases showed at least one connection to another disease, and  <dig>  edges were formed between those diseases .
each node in the network represented a disease.
those nodes belonged to  <dig> top-level do categories and were colored according to their corresponding do categories, such as “respiratory system disease” , “metabolic disease” , “infectious disease” , and so on.
do classified diseases both by anatomical site or system, and by general pathology.
for each of the classifications, despite these different criteria, diseases within one category were usually in close proximity to each other , such as “disease of cellular proliferation” , “disease of mental health” , “nervous system disease” , and so on.fig  <dig> an overview of disease similarity network  based on mednetsim results.
the graph was based on a force-directed layout using the similarity between diseases as attraction force.
nodes were colored according to the top-level do category to which they belong
mednetsim can also identify related disease groups belonging to different do category.
one example of these is myasthenia gravis  which belongs to the “nervous system disease”  category.
figure 9a showed the sub-network around myasthenia gravis .
it is not surprised that we found mg was related with “immune system disease” .
actually, mg is associated with various autoimmune diseases, including thyroid diseases  <cit>  and lupus  <cit> .
thymoma  was found as the strongest associated partner of mg with a mednetsim similarity up to  <dig>  , and vice versa.
the relationship between thymic abnormalities and mg had also been reported  <cit> .
additionaly, mednetsim can also be used to recognize new relatedness between diseases.
fibromyalgia , belonging to the “musculoskeletal system disease”  category, was taken as an example.
as shown in fig.
9b, fibromyalgia was associated to several mental health diseases, e.g., pain disorder , postpartum depression .
studies has shown that fibromyalgia is frequently associated with depression and chronic pain  <cit> .
there were a few reports on the relatedness between fibromyalgia and personality disorder   <cit> .
however, fibromyalgia’s relationship with antisocial personality disorder  and avoidant personality disorder  are currently not reported.
interestingly, their associations were found in fig.
9b.
it was also found that melancholia  was related to fibromyalgia.
those new found relatedness between diseases might deserve further research to understand their common pathophysiology and help drug repositioning research.fig  <dig> the sub-network around myasthenia gravis  and fibromyalgia .
nodes were colored according to membership in the top-level do category.
the thickness of the connections between the nodes reflects the degree of similarity
CONCLUSIONS
methods based on protein interaction networks, literature data , and their integration, were developed to compute disease similarity .
taking advantage of the entire protein interaction network, netsim obtained the best performance in all function-based methods.
among semantic-based methods, the performance of medsim achieved significantly better results.
medsim does not require prior knowledge of disease-associated genes, enabling it to have a wider range of application than the other methods.
medsim can be a great supplement to function-based algorithms, especially when there is not enough gene-disease association data.
the further improved auc of mednetsim shows that integrating biomedical literature and protein interaction data can be an effective way to improve computation for disease similarities.
quality of protein interaction data was found to be more important than its volume, while higher volume of gene-disease association data, even with lower reliability, is more beneficial for disease similarity computation.
in a situation of limited resources, it maybe beneficial to put more efforts toward obtaining more gene-disease association data and improving the quality of protein-protein interaction network.
medsim, netsim and mednetsim are availalbe at http://www.digintelli.com:8000/.
the user can enter two diseases of interest; the web service will compute their similarity and present a corresponding p-value.
additional files
additional file 1: diseasegeneassocsidd.xls.
disease-gene associations acquired from sidd database.
additional file 2: diseasegeneassocdisgenet.xls.
disease-gene associations acquired from disgenet database.
additional file 3: benchmark.xls.
the benchmark set of related disease pairs.
additional file 4: similaritypvalues.xls.
p-values of similarities of disease pairs in the benchmark set and a random set.
additional file 5: similaritynet.xls.
the disease similarity network.
abbreviations
aucthe area under the roc curve
bogbased on overlapping gene sets method
comppicommon protein-protein interactions of hppin and humannet
ctdcomparative toxicogenomics database
disgeneta database of gene-disease associations
dodisease ontology
doiddisease ontology identifier
dsndisease similarity network
gadgenetic association database
gogene ontology
go_bpgo biological process
go_ccgo cellular component
go_mfgo molecular function
hpohuman phenotype ontology
icinformation content
idfinverse document frequency
meshmedical subject headings
mgmyasthenia gravis
micathe most informative common ancestor
nltknature language toolkit
omimonline mendelian inheritance in man
psbprocess-similarity based method
rocreceiver operating characteristic
rwrrandom walk with restart
frfunctional relevance
sidda semantically integrated disease database
tfterm frequency
tf-idfterm frequency times inverse document frequency
umls idunified medical language system identifier
umlsunified medical language system

 discovering closely related diseases could be helpful in revealing their common pathophysiology. it may also be useful for identifying novel drug indications, as similar diseases may have the same or similar therapeutic targets, which suggests they could be treated with the same or similar drugs. there has been a growing interest in quantitatively measuring similarities between diseases. phenotypic similarity plays an important role in a number of biological and biomedical applications. during the past years, based on the human phenotype ontology, researchers had designed several methods to find related diseases and predict disease-causing genes, such as phenomizer, exomiser and phenix. the hpo provides a controlled and standardized vocabulary of phenotypic abnormalities that characterize human diseases. phenotype similarity also, becomes the most common way to define classification rules for diseases. the classification of disease terms in medical subject headings and disease ontology are taking this approach. to quantify disease similarity, several semantic-based methods had thus been proposed based on hpo, mesh or do, such as resnik, lin and wang. resnik’s method measures disease similarity based on information content of the most informative common ancestor between two terms. besides ic of mica, lin’s method also considers the ic of the two compared diseases. wang et al.’s method computes similarity of a disease pair by considering the contribution of all common ancestors in the ontology. it had been successfully applied to compute similarity between mesh terms. all of those semantic-based methods exploited disease associations based on ontologies and/or gene annotations. they did not, however, consider the functional associations between disease-related gene sets. the bog method was thus designed by mathur and dinakarpandian, which calculates disease similarity by exploiting the co-occurrence of disease-related genes. mathur et al. also devised a process-similarity based method. instead of defining disease similarity as a function of genes, psb computes disease similarity based on gene ontology biological process terms associated with those genes. psb achieved a better performance than bog. functional associations between genes involve not only go terms, but also co-expression, protein-protein interaction, etc. cheng et al. recently presented the method funsim, which measures disease similarity using a weighted human protein interaction network. the first neighbors of disease-related genes in the protein network were taken into account. funsim further improved the results of psb. although a number of methods for computing disease similarity had been developed, no method had been proposed to take advantage of the entire protein interaction network, beyond using only the first neighbors. a network-based method is proposed which takes advantage of the entire interaction network. the effectiveness of different data sources were also evaluated, including gene-disease associations and protein-protein interactions. most of the previously developed methods were based on disease-related genes. however, many diseases still have very few or no associated genes. relying entirely on disease-related genes greatly limits the utility of those methods. to overcome the limitation, a new semantic-based similarity measure is developed to compute disease similarity based on the medline database. medsim and netsim were eventually integrated into mednetsim to further improve computing performance. methods diseases and gene-disease association databases the disease terms in do were chosen as the vocabulary for describing diseases. do database is a biomedical resource of disease concepts with stable identifiers organized by disease etiology. it contains non-obsolete disease terms and ‘is_a’ relationships among diseases. the non-obsolete disease terms was used as the disease vocabulary. each disease in do has a unique identifier, called doid. sidd and disgenet were adopted as two disease-gene association databases. sidd integrated five disease-related gene databases: generif, online mendelian inheritance in man, comparative toxicogenomics database, genetic association database, and splicedisease. in total, sidd contains diseases and gene-disease associations. the disgenet database integrated human gene-disease associations from various expert curated databases and text-mining derived associations including mendelian, complex and environmental diseases. compared to sidd, disgenet had more lower reliability disease-gene associations based on literature mining, i.e., lhgdn and befree data. disgenet contains diseases and gene-disease associations. umls id was used as the unique identifier for each disease in disgenet. we mapped umls id to doid, which produced disease terms and gene-disease associations. almost every disease term in disgenet has more associated genes than that in sidd. all source data were downloaded until april fig gene-disease association databases. a: the number of diseases, b: the number of associations between genes and diseases protein interaction datasets two protein interaction datasets were used. one is hppin, built in house, which integrated four existing protein interaction databases, i.e., biogrid, hprd, intact, and homomint. protein identifiers were mapped to the genes coding for the proteins, and redundant interactions were removed. the acquired protein interaction network covered human genes and interactions. the other is humannet, which is a genome-scale functional network for human genes. to build humannet, diverse functional genomics and proteomics datasets were evaluated for their tendencies to link human genes in the same biological processes. pairwise gene linkages derived from the individual datasets were then integrated into a comprehensive humannet. humannet contains functional linkages among human genes. unlike hppin which mainly focuses on experimentally verified protein interactions, humannet was constructed based on the functional probability that two genes belonged to the same biological processes. the two protein interaction datasets have genes and interactions in common. additionally, different proportions of hppin were randomly sampled times and used as the protein interaction datasets to evaulate the impact of data volume on the proposed method.fig protein interaction datasets. a: the number of genes, b: the number of interactions between genes, *: the common protein-protein interactions medline-based disease similarity biomedical literature contains rich and diverse information, such as disease symptoms, pathogenesis, therapeutic drugs, and so on. features representing diseases were generated through mining the biomedical literature corpus; the features were then utilized to compute disease similarity. medsim was not limited to use only one aspect of disease information, but took advantages of all relevant information that had already been archived in the literature.fig overview of medsim. do: human disease ontology database; umls: unified medical language system disease corpus the text corpus contains all medline abstracts published up to year the non-obsolete disease terms in do were used as the disease vocabulary. each disease term was mapped to unified medical language system so that its synonyms could be retrieved. synonyms were taken directly from do for diseases that could not be mapped to umls. every disease term and its synonyms were then used as keywords to perform keyword-based queries into medline to retrieve abstracts related to that disease. to limit computational cost, only the top most relevant abstracts were selected to construct the bag-of-words model for diseases. the relevance of an abstract to a disease was defined in eq.  rabstract=∑wwdf×wof where wdf and wof represent document frequency and occurrence frequency of a word x, respectively. document frequency wdf is the proportion of abstracts that contain word x. wdf represents the relevance of word x to a disease. occurrence frequency wof represents the number of times word x occurs in an abstract, measuring the importance of word x in a specific abstract. for a specific disease, w is defined as the set of nouns which appeared in abstracts when wdf is greater than. larger rabstract means that an abstract is more closely related to the disease. some diseases were not yet broadly studied, so their number of retrieved abstracts can be less than for those cases, all retrieved abstracts were used. for each disease, the selected most relevant abstracts were merged into one combined document. at the end of preprocessing, every disease was associated with one document. these documents together made up the disease corpus. constructing the bag-of-words model and computing medsim the disease corpus was tokenized to obtain word vocabulary, using python package nltk to remove non-alphabetic words and reduce inflected/derived words to their stem. overly common or rare words were removed, as those words could not provide meaningful information. each disease was then represented by a word vector, whose dimensionality is the size of the word vocabulary. each dimension was assigned a weight based on term frequency and inverse document frequency values. tf is the number of times a word appears in a document. idf represents the inverse of the number of documents containing the word. tf-idf assigns larger weights to words that appeared more often in a document but only in a small percentage of all documents, as those words are important and informative for that document. with diseases represented as tf-idf weighted vectors, the medsim of two diseases was measured by calculating the cosine similarity of the two vectors. python package scikit-learn was used to perform the computation. network-based disease similarity previously published methods weren’t designed to utilize the entire protein interaction network. they instead focused only on the disease-related genes or their first neighbors in the network. to take full advantage of the entire protein interaction network, random walk with restart was used to measure functional relevance between a gene g and a gene set g, which is described in eq.  frg={prwrg∈proteininteractionnetwork1g∉proteininteractionnetworkandg∈g0g∉proteininteractionnetworkandg∉g) where gene set g was defined to be the seed genes, that is, the known set of genes associated with a disease. the initial probability of each seed genes was set to. prwr represents the acquired steady-state probability of gene g after running rwr in the whole protein interaction network. a larger probability ) will be assigned to gene g when it sits more closely to the gene set g in the network according to eq.  which means that gene g are more functionally related with gene set g. suppose that g1 = {g g …} and g2 = {g g …} are the seed gene sets for disease d and d respectively. then, the netsim of d and d is defined in eq.  nesimg1g2=∑1≤i≤leng1frg2g1i+∑1≤j≤leng2frg1g2jleng1+leng g1i∈g g2j∈g where len and len are the number of genes in g and g respectively. the numerator is the sum of functional relevance of g1i to g and g2j to g a higher netsim value represents closer connection between g and g which suggests closer ties between diseases d and d medsim and netsim is combined into mednetsim, which is defined in eq.  mednetsimd1d2=medsimd1d2×netsimg1g where d and d are two diseases in do, g and g are the seed gene sets for d and d respectively. performance evaluation similarities of disease pairs in the benchmark set and the random set were calculated and ranked in descending order, receiver operating characteristic curves were then drawn to evaluate and quantify the predictive power of the proposed methods. a roc curve is a plot of the true positive rate of a classifier as a function of the false positive rate. the area under the roc curve is used as a quantitative measure of a classifier’s quality. disease pairs in the benchmark set and the random set are defined as positives and negatives, respectively. true positives are the disease pairs in the benchmark set that are correctly predicted by a classifier, and false positives are those disease pairs from the random set that are predicted to be positives but not found in the benchmark set. more percentage of disease pairs in the benchmark set receiving higher rankings means better auc values. the benchmark set was taken from reference. it had diseases and disease pairs with high similarity derived from two manually checked datasets by suthram et al. and pakhomov et al.. cancers were omitted. the benchmark set contains disease pairs that are expected to be related to each other, such as alzheimer’s disease and schizophrenia, diabetes mellitus and obesity. it also includes some pairs that are not apparently related, but were found to be correlated by various evidences, such as asthma and diabetes mellitus, malaria and anemia. disease pairs were randomly selected from do to generate a random set, with disease pairs from the benchmark set removed from the generated random set. to get an average auc of the proposed methods, the above experiment was iterated times by calculating similarities of disease pairs in the benchmark set and random sets. medsim was compared with other semantic-based methods including resnik, lin and wang, based on hpo and do, respectively. for each disease, the associated hpo annotations were acquired from, which covered disease-phenotype associations for over common, rare, infectious and mendelian diseases through text-mining approach. the hpo-based disease similarities were defined by calculating the semantic similarity of their associated hpo phenotypes. for two diseases, the hpo-based similarity of d to d is defined as follows: hpo_simd1→d2=avg∑s∈d1maxt∈d2semsimst where s and t are the annotated phenotypes of d and d respectively. semsim() is one of the methods applied to compute the semantic similarity of two phenotype terms, including resnik, lin and wang. eq.  for each phenotype term of d found the “best match” among the phenotype terms annotated to d and the average overall phenotype terms was calculated. note that this similarity is asymmetric, i.e., hpo_sim is not always equal to hpo_sim. therefore, we used a symmetric hpo-based similarity, which is defined in eq. 6: hpo_simd1d2=12hpo_simd1→d2+12hpo_simd2→d the do-based disease similarities were defined as the directly semantic similarity of two disease terms in do, where the above mentioned three semantic-base methods were applied, too. netsim was also compared with other function-based methods including bog, psb and funsim. parameters of the aforementioned methods were set to values used in the original paper. constructing disease similarity network disease terms from do were used as nodes in the similarity network between diseases. we computed the pair-wise similarity for a total of diseases by the proposed method mednetsim. if the similarity of a disease pair was ranked in the top  %, an undirected weighted edge between the disease pair was drawn. the network was visualized with the force-directed layout algorithm of cytoscape and colored according to top-level do categories.  utilizing the entire network benefits disease similarity computation similarities of disease pairs in the benchmark set and a random set were calculated by netsim and other function-based methods. as shown in fig. 4a, the bog method, with an auc of  %, had the worst performance among function-based methods. linking genes based on the go biological process ontology, psb method had significantly improved performance, achieving an auc of  %. considering nearest neighbors of disease-related genes in protein interaction network, funsim improved its auc to  %. the proposed method, netsim, which utilized the entire protein interaction network, further improved its auc to  %. the results show that utilizing the entire network can increase computing performance for disease similarity calculation. integrating medsim and netsim, the mednetsim achieved the highest auc among all function-based methods, improving its auc to  %. the performance improvement indicates that integration of medline and protein interaction network can be an effective way to compute disease similarities. to check the stability of netsim and mednetsim, the above computation was repeated times by calculating similarities using randomly generated disease pair sets. fig 4b shows the average auc of bog, psb, funsim, netsim and mednetsim, which is consistent with fig. 4a.fig performance of function-based methods. a roc curves for the benchmark set and a random set. b average auc for the benchmark set and random sets the mednetsim similarity values of all disease pairs were computed, and a distribution of, similarity values was acquired. the ranking of a similarity value in the distribution was used to compute its corresponding p-value. if the mednetsim similarity value of a disease pair is in the highest-ranking 5 % of the distribution, the two diseases are considered related. to evaluate the ability of mednetsim in discriminating positive and negative cases, the p-values of similarities of disease pairs in the benchmark set and a random set were calculated. for the benchmark set, disease pairs were recognized as highly related diseases correctly and disease pairs did not show a significant p-values. the false negatives can be divided into two groups. the first group had a non-significant p-value of medsim similarity, but a significant p-value of netsim similarity, e.g., polycystic ovary syndrome & myocardial infarction, malaria & epilepsy syndrome. the missed calling of being positives for those disease pairs was mainly due to the very bad results of medsim. that is to say, the research literature contains less information about their relatedness, therefore dragging down the performance of mednetsim. for those disease pairs, netsim may be a better choice. in the second group, both medsim and netsim similarities did not show significant p-values. a representative disease of the second group was lipid storage disease. out of the disease pairs between lipid storage disease and other diseases in the benchmark set were incorrectly identified, e.g., lipid storage disease & obesity, lipid storage disease & diabetes mellitus. the number of associated genes of obesity and diabetes mellitus was and, respectively. lipid storage disease only had associated genes. out of the associated genes, and genes were shared by obesity and diabetes mellitus, respectively. although more than 1/ associated genes of lipid storage disease appeared in obesity and diabetes mellitus, they still got a bad netsim results. that is because obesity and diabetes mellitus had a much bigger number of associated genes than lipid storage disease. this indicates that netsim performs less well when two diseases have a large difference in the number of disease-associated genes. for the random set, out of disease pairs were recognized as related diseases. more than half of the disease pairs were cancer related diseases, e.g., penile neoplasm & cecum cancer, pancreatic cancer & tubular adenocarcinoma. as cancer diseases were omitted in selecting benchmark set, it is not surprising that so many disease pairs related to cancers are detected as false positives. the relatedness of diseases belonging to different top-level do categories was also identified, e.g., essential hypertension & hyperthyroidism. recently, emokpae et al. had pointed out that hyperthyroidism was the most common thyroid disorder observed in patients with essential hypertension. it indicates that our method can recognize related diseases which apparently seem unrelated. in addition, the relationship of impulse control disorder & narcissistic personality disorder was also detected. the two disease are both in the “disease of mental health” category, but there is no report on their relatedness. therefore, mednetsim can also discover new unknown relatedness among diseases.table examples of false negatives and false positives with p-values from mednetsim medsim can be a useful supplement to function-based methods roc curves of medsim and other semantic-based methods based on hpo and do, respectively, were also generated. for the methods based on hpo, lin’s method had the worst performance with an auc of only  %, and wang et al.’s method acquired the best performance among the three methods. as hpo was replaced by do to calculate disease similarity, resnik’s method became the worst method, and wang et al.’s method still had the best performance with an auc of  %. overall, performances of hpo-based methods are similar to do-based methods. however, compared to computing disease similarity based on ontologies, the proposed medsim had a significantly better performance than those methods. medsim achieved an auc of  %, which is even slightly better than the function-based method bog. figure 5b shows the average auc for all semantic-based methods. the result is consistent with fig. 5a.fig performance of semantic-based methods. a roc curves for the experimental results on the benchmark set and a random set. b average auc for the benchmark set and random sets. hpo_res, hpo_lin and hpo_wang denoted disease similarities computation by using resnik, lin and wang based on hpo, respectively two reasons may explain why medsim achieved the best performance among semantic-based methods. on the one hand, previous methods suffered from the incompleteness of ontologies and the lack of coverage of gene-disease or phenotype-disease association data. for example, only one-third of do diseases have associated genes. hpo is widely used in the rare disease community. however, the infrastructure of phenotype data for common and infectious diseases  is still developing. on the other hand, medsim considered much richer and more diverse information included in literature, not only disease-related genes, but also disease symptoms, pathogenesis, therapeutic drugs, and so on. medsim requires only biomedical literature, no requirement to know disease-associated gene sets and ontologies. it thus has much broader applicability than previously published methods, especially in the case of no sufficient gene-disease association data. the impact of different data sources gene-disease association databases the effectiveness of different gene-disease association data was evaluated. disgenet was used as a replacement for sidd. compared to sidd, disgenet has much more lower reliability associations based on literature mining. its disease-gene associations are nearly two times of those in sidd, with only 34 % more disease terms. using disgenet as gene-disease association data source, the auc of netsim grew to  %, which is even better than mednetsim that fused medsim and netsim. integration of medsim and netsim_dgn produced an auc of  %. fig. 6b shows the average auc of netsim_dgn and mednetsim_dgn, which is consistent with fig. 6a too. the above observations show that a richer gene-disease association data, even with a lower reliability, is favorable for discovering relatedness between diseases.fig the impact of different data sources. a roc curves for the experimental results on the benchmark set and a random set. b average auc for the benchmark set and random sets protein interaction datasets to gauge the impact of different interaction datasets on computing performance, humannet database was used as the protein interaction network, substituting hppin. the number of protein nodes in humannet and hppin do not differ greatly, but the number of interactions in humannet is more than three times that of hppin. however, the performance of netsim while using humannet did not improve at all compared to using hppin, with both achieving an auc of  %. furthermore, the common interaction pairs of hppin and humannet were also applied as the protein interaction network to evaluate the performance of netsim. although comppi had a much smaller dataset than hppin or humannet, netsim_comppi achieved the same performance as netsim and netsim_hn, with an auc of  % too. the average auc of netsim_hn and netsim_comppi also showed the same results. additionally, the average auc of netsim with different proportions of hppin were also computed. as shown in fig.  the average auc increased rapidly at the beginning, it then leveled off and did not grow as fast once the sampling rate hit 60 %. the average auc plateaued at a sampling rate of 80 %. the above results indicate that merely using more protein interaction data does not lead to improved performance of netsim. it might partially explain why using humannet, which has more than three times protein interaction data than hppin, did not improve the performance of netsim.fig the average auc of netsim with different proportion of hppin sampled percentage of interaction pairs sharing go annotation was analyzed for humannet, hppin and their common protein interactions. for the entire go annotation and its three categories, the percentage of pairs sharing annotation in hppin was higher than that in humannet, suggesting hppin has a higher data quality than humannet. the fact that humannet did not achieve improved performance for netsim may partially be due to humannet’s lower data quality than that of hppin. in addition, whether the entire go or its three categories, comppi had the highest percentage of protein pairs sharing annotation in the three datasets, indicating that comppi has the best data quality. the highest data quality of comppi may be responsible for it acquiring same performance as that of hppin or humannet. all those results suggest that the quality of protein interaction data is more important than its volume for the computation of disease similarity.table percentage of interaction pairs sharing go annotation comppia go gene ontology, go_bp biological process, go_cc cellular component, go_mf molecular function athe common protein-protein interactions between humannet and hppin disease similarity network as shown in fig.  a disease similarity network was generatedty based on mednetsim from the top-ranking  % of pair-wise similarity values among diseases in do. of the diseases showed at least one connection to another disease, and edges were formed between those diseases. each node in the network represented a disease. those nodes belonged to top-level do categories and were colored according to their corresponding do categories, such as “respiratory system disease”, “metabolic disease”, “infectious disease”, and so on. do classified diseases both by anatomical site or system, and by general pathology. for each of the classifications, despite these different criteria, diseases within one category were usually in close proximity to each other, such as “disease of cellular proliferation”, “disease of mental health”, “nervous system disease”, and so on.fig an overview of disease similarity network based on mednetsim results. the graph was based on a force-directed layout using the similarity between diseases as attraction force. nodes were colored according to the top-level do category to which they belong mednetsim can also identify related disease groups belonging to different do category. one example of these is myasthenia gravis which belongs to the “nervous system disease” category. figure 9a showed the sub-network around myasthenia gravis. it is not surprised that we found mg was related with “immune system disease”. actually, mg is associated with various autoimmune diseases, including thyroid diseases and lupus. thymoma was found as the strongest associated partner of mg with a mednetsim similarity up to, and vice versa. the relationship between thymic abnormalities and mg had also been reported. additionaly, mednetsim can also be used to recognize new relatedness between diseases. fibromyalgia, belonging to the “musculoskeletal system disease” category, was taken as an example. as shown in fig. 9b, fibromyalgia was associated to several mental health diseases, e.g., pain disorder, postpartum depression. studies has shown that fibromyalgia is frequently associated with depression and chronic pain. there were a few reports on the relatedness between fibromyalgia and personality disorder. however, fibromyalgia’s relationship with antisocial personality disorder and avoidant personality disorder are currently not reported. interestingly, their associations were found in fig. 9b. it was also found that melancholia was related to fibromyalgia. those new found relatedness between diseases might deserve further research to understand their common pathophysiology and help drug repositioning research.fig the sub-network around myasthenia gravis and fibromyalgia. nodes were colored according to membership in the top-level do category. the thickness of the connections between the nodes reflects the degree of similarity  methods based on protein interaction networks, literature data, and their integration, were developed to compute disease similarity. taking advantage of the entire protein interaction network, netsim obtained the best performance in all function-based methods. among semantic-based methods, the performance of medsim achieved significantly better results. medsim does not require prior knowledge of disease-associated genes, enabling it to have a wider range of application than the other methods. medsim can be a great supplement to function-based algorithms, especially when there is not enough gene-disease association data. the further improved auc of mednetsim shows that integrating biomedical literature and protein interaction data can be an effective way to improve computation for disease similarities. quality of protein interaction data was found to be more important than its volume, while higher volume of gene-disease association data, even with lower reliability, is more beneficial for disease similarity computation. in a situation of limited resources, it maybe beneficial to put more efforts toward obtaining more gene-disease association data and improving the quality of protein-protein interaction network. medsim, netsim and mednetsim are availalbe at http://www.digintelli.com:8000/. the user can enter two diseases of interest; the web service will compute their similarity and present a corresponding p-value. additional files additional file 1: diseasegeneassocsidd.xls. disease-gene associations acquired from sidd database. additional file 2: diseasegeneassocdisgenet.xls. disease-gene associations acquired from disgenet database. additional file 3: benchmark.xls. the benchmark set of related disease pairs. additional file 4: similaritypvalues.xls. p-values of similarities of disease pairs in the benchmark set and a random set. additional file 5: similaritynet.xls. the disease similarity network. abbreviations aucthe area under the roc curve bogbased on overlapping gene sets method comppicommon protein-protein interactions of hppin and humannet ctdcomparative toxicogenomics database disgeneta database of gene-disease associations dodisease ontology doiddisease ontology identifier dsndisease similarity network gadgenetic association database gogene ontology go_bpgo biological process go_ccgo cellular component go_mfgo molecular function hpohuman phenotype ontology icinformation content idfinverse document frequency meshmedical subject headings mgmyasthenia gravis micathe most informative common ancestor nltknature language toolkit omimonline mendelian inheritance in man psbprocess-similarity based method rocreceiver operating characteristic rwrrandom walk with restart frfunctional relevance sidda semantically integrated disease database tfterm frequency tf-idfterm frequency times inverse document frequency umls idunified medical language system identifier umlsunified medical language system 
a number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. in this study, we propose a new method for computing disease similarity by integrating medical literature and protein interaction network. mednetsim consists of a network-based method, which employs the entire protein interaction network, and a medline-based method, which computes disease similarity by mining the biomedical literature. integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. it was found that quality of protein interaction data was more important than its volume. it may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. disease similaritymedsimnetsimmednetsimrandom walk with restarthttp://dx.doi.org/ /501100001809national natural science foundation of china61179008yu jingkai issue-copyright-statement© the author 2016 disease similarities can be computed using the proposed methods at http://www.digintelli.com:8000/. its average auc reached  %. medsim, whose performance was even comparable to some function-based methods, acquired the highest average auc in all semantic-based methods. integration of medsim and netsim further improved the average auc to  %. utilizing higher volume of disease-related gene data further improved the average auc of mednetsim and netsim to  % and  %, respectively. most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. on the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. we further studied the effectiveness of different data sources.
 identifying relatedness among diseases could help deepen understanding for the underlying pathogenic mechanisms of diseases, and facilitate drug repositioning projects. a number of methods for computing disease similarity had been developed; however, none of them were designed to utilize information of the entire protein interaction network, using instead only those interactions involving disease causing genes. most of previously published methods required gene-disease association data, unfortunately, many diseases still have very few or no associated genes, which impeded broad adoption of those methods. in this study, we propose a new method for computing disease similarity by integrating medical literature and protein interaction network. mednetsim consists of a network-based method, which employs the entire protein interaction network, and a medline-based method, which computes disease similarity by mining the biomedical literature.  among function-based methods, netsim achieved the best performance. its average auc reached  %. medsim, whose performance was even comparable to some function-based methods, acquired the highest average auc in all semantic-based methods. integration of medsim and netsim further improved the average auc to  %. we further studied the effectiveness of different data sources. it was found that quality of protein interaction data was more important than its volume. on the contrary, higher volume of gene-disease association data was more beneficial, even with a lower reliability. utilizing higher volume of disease-related gene data further improved the average auc of mednetsim and netsim to  % and  %, respectively.  integrating biomedical literature and protein interaction network can be an effective way to compute disease similarity. lacking sufficient disease-related gene data, literature-based methods such as medsim can be a great addition to function-based algorithms. it may be beneficial to steer more resources torward studying gene-disease associations and improving the quality of protein interaction data. disease similarities can be computed using the proposed methods at http://www.digintelli.com:8000/. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords disease similaritymedsimnetsimmednetsimrandom walk with restarthttp://dx.doi.org/ /501100001809national natural science foundation of china61179008yu jingkai issue-copyright-statement© the author 2016 