BACKGROUND
the phenome, which can be defined as the comprehensive collection of phenotypic information  <cit> , has been studied intensively to provide solutions for disease-centric biological problems  <cit> .
most studies in this area aim to demonstrate the correlation between genome and phenome, where various approaches have indicated the usefulness of the phenome to arrive at conclusions about diseases .
for example, a global comparison of the human interactome and phenome through network alignment has also shown that genome-phenome associations can explain causalities on a larger scale  <cit> .
these studies demonstrate that the phenome can be mined by appropriate tools to discover new treatment opportunities for diseases.
drug repositioning often involves a process of using knowledge accumulated about a drug during the de novo drug discovery process, clinical trial, and/or post-marketing surveillance to identify new therapeutic purposes other than the originally intended purpose.
examining drugs with known safety profiles and pharmacokinetic properties can lead to new therapeutic indications more quickly and with less risk.
the history of successful drug repositioning is comprised mostly of serendipitous findings, such as alternative indications for sildenafil and thalidomide, but a more systematic approach is advocated to explore the full benefit of this approach.
interest in drug repositioning is increasing and has attracted researchers from academia, government, and industry, many of whom have developed in silico solutions to assist repositioning research.
these in silico approaches demonstrate the potential of systematic study to improve drug repositioning efforts.
in general, the reported studies can be classified as either disease-centric or drug-centric approaches  <cit> .
most of them used molecular, genomic, or phenotypic data .
as an example of a molecular study for drug repositioning, keiser et al.
measured the chemical similarities of drugs consisting of both us food and drug administration -approved and investigational drugs and linked the results to drug targets.
they reported thousands of potential drug-target associations and experimentally validated  <dig> of them that may add alternative therapeutic options for diseases  <cit> .
using genomic data, iorio et al.
assessed drug similarity based on drug-elicited gene expression in cell lines with a network analysis approach.
their work suggested that fasudil would be effective in the treatment of autophagy, which is a major process in cancer, and this was confirmed experimentally  <cit> .
in a separate study with genomic data, sirota et al.
compared the gene expression profiling elicited by drugs and that profiled for diseases.
they considered a drug effective for a disease if the expression profiles reversely matched.
a supporting animal study verified that citemedine could be effective for lung cancer  <cit> .
in a follow up study using the same approach, they reported that anticonvulsant topiramate was effective in the treatment of inflammatory bowel disease   <cit> .
on the other hand, the use of the phenome to identify new therapeutic treatments has also been explored in the research community.
for instance, campillos et al.
hypothesized that drugs having common side effects can also treat the same disease and examined  <dig> drug-drug pairs, of which nine were experimentally verified for alternative therapeutic uses  <cit> .
yang et al.
also studied side effects to assess their associations with diseases through statistical tests  <cit> .
they further focused on the drugs that showed a particular side effect but was not mentioned with the strongly associated indication.
current in silico methodologies in drug repositioning, including phenome-based techniques  <cit> , mostly rely on drug-drug similarity measurements which can lead to guilt-by-association  <cit> .
in other words, the search space is often restricted to the most similar drug without taking full advantage of the information embedded in the entire dataset.
we proposed that the phenome should be explored with a probabilistic generative model for a comprehensive analysis.
such an analysis may reveal further links between drugs and diseases.
one important consideration for use of the phenome data is its quality.
although a drug’s phenome  should be consistent within a specific population, the information recorded in some sources, may still remain partial.
the missing information could be due to the lack of studies and resources to comprehensively and accurately record all the possible side effects and indications of a drug.
given the challenges to gather a reliable phenome collection, sider   <cit>  stands as a well-credited data source with a room to exploit partial knowledge.
in other words, some of alternative indications of a drug that are not yet captured in the existing phenome database are ready for discovery, which is the objective of this study.
this study is built on two premises:  the allocation of phenotypes in a phenome arising through both on-target and off-target methods follow a probabilistic distribution in which the therapeutic indications of drugs are embedded and  the inherent probabilistic relationship of a drug’s phenome is insensitive to the incompletion of the phenome source.
thus, a probabilistic graph model can be built using the partial phenome and used to identify repositioning opportunities.
to build probabilistic associations between existing drugs and possible indications, we used a bayesian model, latent dirichlet allocation , which was primarily developed for topic modeling of text documents.
with its probabilistic nature, lda can link documents and words through latent variables known as topics.
lda achieves such relations by manipulating words, which are the only observed variables, in a bayesian setting where documents are assumed to be a weighted mixture of multiple topics  <cit> .
by analogy, we have extended this procedure to drugs and phenotypes since phenotypes are observable outcomes of drugs after they hit intended/unintended targets.
in other words, this mode of action  mimics like a latent process where therapeutic effects occur along with side effects due to unknown cellular mechanisms.
since we cannot holistically observe the mechanism causing both effects, we may rely on hidden variables that play a role in the observed outcomes.
therefore, phenotypes are said to be probabilistically distributed across drugs by hidden variables and the same distribution can be utilized to complete missing information.
more specifically, these hidden variables  can let us discover the potential links between drugs and phenotypes, specifically indications.
lda has previously been applied in biological studies such as discovering relationships in pubmed articles, mining relational paths in the biomedical data, and grouping fda approved drugs based on their therapeutic uses and safety concerns .
however, its ability to suggest potential associations has yet to be investigated for drug repositioning.
in this study, we applied lda to the sider database to approximate the phenome distribution of drugs.
to develop a criterion for suggesting new uses of drugs, we first observed the recovery potential of the model by predicting each “missing” indication from the phenome that was deliberately switched to unknown.
in doing so, we took the advantage of not only remaining indication information, but also the whole side effect profile, which is another descriptive feature of a drug.
then, we required a prediction to be both significant and ranked within the top-k positions, where k is equal to the number of known indications for a given drug.
we refer to this last characteristic as being within the indication space  of a drug.
imposing the same criteria to our initial drug-phenome matrix without any perturbation, we further employed this procedure for drug repurposing.
in other words, we attempted to make use of our partial knowledge about side effects and indications to systematically cut down the possible number of drug-indication pairs that can suggest alternative indications.
finally, we both examined several suggested uses by supportive evidence in the literature and captured the original indications of some drugs whose indications were not recorded in sider.
RESULTS
as depicted in figure  <dig>  this study consists of the following steps:  building a drug-phenome matrix with the data profile obtained from sider;  determining the optimal number of topics by using an information loss criterion to derive a lda model;  determining the criteria of the model for application with a procedure that assumed the absence of each indication in a one-by-one fashion;  applying lda on the drug-phenome matrix to get probabilities for empty cells, validating the approach by finding known indications mislabeled in sider; and predicting new indications for given drugs and validating them through various sources.figure  <dig>
overview of the study.
a) integrating side-effects and indications to complete phenome; b) determining the number of topics by using information loss; c) hiding known indications one by one to see the recovery potential for decision criteria; d) developing the decision criteria considering recovered indications with significance; e-f) lda is applied to the drug-phenome matrix.
observing the decision criteria, real indications were recovered for drugs without indication information and new indications suggested for remaining drugs.
number of topics
we computed the optimal number of topics for the drug-phenotype matrix by using an information loss criterion   <cit> .
when the objective function is minimized, the corresponding number of topics is considered to be the optimal.
the information loss reached its minimum when the number of topics hit  <dig>
developing decision criteria
as shown in figure 1c, a total of  <dig>  cases were tested to assess the recovery rate for the masked indications.
specifically, an entry of “1” in the matrix was replaced by “0” one at a time, and then lda was conducted on the remaining entries and the conditional probability of the indication for the given drug p along with the position of the indication in a ranked list based on probabilities for all indications p were calculated.
in order to develop a decision criterion before suggesting alternative indications, we considered the probability of a prediction against chance as well as its location depending on a drug’s is.
first, as shown in figure 1d, we rejected predictions below random chance , which identified  <dig>  cases that correspond to bars above  <dig>  in figure  <dig>  next, the  <dig>  cases were further analyzed against each drug’s is.
in  <dig>  out of  <dig>  cases, the held indication was recovered correctly to be within the drug’s is, yielding an average success rate of 70%  within the portion of pairs that has greater probability than the random chance.figure  <dig>
binned probabilities for known indications in  <dig>  cases.
p stands for the probability of the indication in a drug-indication pair.
each bar denotes the number of known pairs falling into the probability intervals.
blue bars show the cases over random chance , and we have  <dig>  drug-indication pairs satisfying this condition.
of note, the individual success rate  varied on a drug’s is; the rate was lower in the case of drugs with fewer known indications.
for example, we calculated the success rate by incrementally removing the drugs with the lower number of indications.
an increasing success rate from 70% to 100% was observed for recovering indications.
the results demonstrated that high rates were mostly associated with drugs that have a higher number of indications.
retrieving original indications
after developing the criteria, which gave a success rate of 70% for the masked indications, we ran lda on the drug-phenome matrix in order to discover new links between drugs and indications.
this section reports findings for the retrieval of original indications and alternative indications for drug repositioning.
the sider database has indication information for most drugs along with side effect profiles.
however,  <dig> of the drugs in the database were not associated with any indications, even though  <dig> of them had known therapeutic uses.
we suspected that these  <dig> compounds should have multiple indications.
therefore, we employed our model to suggest the missing indication.
we relied on the same decision criteria, but assumed that the is =  <dig>  which was the average number of indications in the data set.
for each of the  <dig> drugs, we examined the top  <dig> indications whose probabilities were higher than the threshold.
in order to verify drug-indication pairs, we reviewed the reports from the fda-approved drug labels, drugbank  <cit> , and the scientific literature.
we confirmed that  <dig> drugs were shown to be effective for at least one indication suggested by the model.
in fact, for  <dig> of these, the model captured the original approved indications .table  <dig>
indications retrieved by the model
findings supported by literature
drug
indication
rank
reference
suggesting alternative indications for drug repositioning
containing  <dig> drugs with  <dig>  indications, sider provides a sparse representation of drugs and their indications, which implies that there are many unknown, but possible, drug-indication associations  to be investigated.
in that regard, our model worked as a screening technique that might help to reduce the number of candidates for further explorations.
as a matter of fact, relying on the established criteria , our model brought up  <dig>  potential drug-indication pairs  that correspond to  <dig>  % of the total number of possible pairs.
for the suggested  <dig>  drug indication pairs, the relationship between indication space  and p is illustrated in figure  <dig>  the figure also provides the number of drug-indication pairs for the drugs, which were grouped based on the is bins they fall in.
these two illustrations show that the number of pairs and probabilities for the pairs with lower is are greater.
this observation implies that for the drugs with smaller is, the model can suggest alternative indications that are being highly probable and worth to investigate, although it was a challenging task to recover masked indications for the same drugs.figure  <dig>
binned  <dig>  suggested pairs based on is along with probabilities.
x-axis denotes the number of indications and the bars represent the number of cases  including drugs with that many indications.
box plots summarize the probability distribution  of suggested drug-indication pairs.
the model offered new treatment options to be investigated for  <dig> drugs for which two criteria mentioned earlier was satisfied.
when the alternative indications were examined, some of the suggestions were confirmed.
table  <dig> shows a partial list of these confirmations with supportive sources for the new indications that were ranked within the top  <dig> among  <dig>  indications.
for instance, amantadine is currently used as medication for influenza a virus and our model suggested alternative uses including the treatment of epilepsy.
we conducted a literature search for this suggested treatment option and found that amantadine had been studied and had been beneficial in reducing seizures  <cit> .
additionally, atazanavir was suggested for use in hiv infection, which is an fda-approved indication  <cit>  but is not mentioned in sider.table  <dig>
verification of suggested uses through literature
discussion
experimentally evaluating alternative therapeutic uses of all marketed drugs is time-consuming and labor-intensive.
thus, developing an in silico methodology for drug repositioning is a cost-effective way to move the field forward.
in this study, we proposed an in silico framework which aims to elaborate the entire observed phenome of the available drugs by a probabilistic graphical model.
using lda, we identified the probabilistic associations of drugs and phenotypes through a latent variable .
under two established decision criteria, we applied the method to infer the probability of new indications for a given drug.
the methodology quantifies the nature of a possible relationship that is not previously known.
furthermore, it does not require any a priori information because of its unsupervised nature.
another notable advantage of lda is its flexibility to link a drug with multiple latent variables  through which drugs can be linked to various indications, including those that do not exist in sider.
we first established the criteria for the use of lda on whole phenome by first assuming the absence of each known drug-indication pair and making predictions for that particular indication.
for each case, we checked whether the probability ) was greater than random chance  and the indication was ranked within the range of known indications for the given drug .
since q =  <dig>  is a deterministic threshold for all predictions, it is not allowed to vary with the success ratio that may lead to overfitting.
indeed, its impact remains more unbiased with respect to higher thresholds .
on the other hand, we primarily employed is as a parametric measure to determine a drug-specific degree of freedom, which enabled us to assess the validity of the model for known pairs and to make a fair comparison.
the results show that success rate of the model mostly depends on the number of known indications, with higher rates of success for drugs with many known indications.
this is likely because the greater amount of information increased our ability to identify hidden relationships.
nonetheless, the model performed well even on those drugs with fewer indications, with a minimum of 70% success for drugs with at least one known indication.
the capacity of the model to retrieve held-out indications demonstrates its applicability in real-world scenarios.
thus, we made an attempt to retrieve indications for drugs that had been mistakenly omitted in the original dataset from sider.
of the  <dig> drugs with known therapeutic uses in this group, the model was able to identify an indication that had supporting evidence for  <dig> of them; for  <dig> of these  <dig>  the indication identified was the approved indication for the drug.
the final goal of the model is to identify novel uses for existing drugs.
this model identified  <dig> drugs from sider with new potential indications for further investigations.
similar to the earlier studies, we provided potential pairs that might require an exhaustive search for evidence to verify that the pairs were promising.
therefore, we performed a co-occurrence search in pubmed for the drug-indication pairs and observed that 75% of the pairs were mentioned together in an abstract at least once .
even though co-occurrence cannot be a proof of therapeutic effect alone, it implies the relatedness of many such pairs.
then, since we could not go through the whole list, we made an effort to examine several of the proposed repositioning opportunities to determine if any supporting evidence for the indication existed.
we examined the scientific literature, clinical trial data, and approved drug labels to find supportive evidence for the suggested use.
the prediction of alternative indications for a drug summarized in table  <dig> is based on one bayesian measure, i.e., the conditional probability of a particular alternative use for a given drug ).
the bayesian characteristics also allow ranking drugs for queried indications; drugs can be ordered based on the conditional probability p. using the same strategy, drugs falling within the top k positions  hold the potential for treatment of a queried disease.
one rare disease, cystic fibrosis, was queried for a candidate drug.
our model suggested ceftazidime for the treatment option, which was indicated earlier in the literature, but was mentioned in neither drug labels nor sider  <cit> .
of note, the p measure failed to identify the alternative use of ceftazidime for cystic fibrosis, but such an association was uncovered by using the p measure.
the results indicate the utility of both measures for revealing repositioning opportunities.
among the drugs meeting our decision criteria was thalidomide, a well-known example of a repositioned drug.
the model correctly identified the new indication of multiple myeloma for thalidomide.
the model, however, is constrained in some respects by our choice of data source.
although sider is intended as a comprehensive resource for side effects and indications, not all known indications are included, and these missing indications may affect the performance of the model.
for example, buproprion is a well-known example of serendipitous drug repositioning.
initially approved as an anti-depressant , clinical observation indicated its potential for smoking cessation.
we were unable to identify this new indication using lda, because this indication does not appear for any drugs in sider.
this is reasonable that because no drug in the dataset is associated with smoking cessation it is not possible to identify that as an association using only this data set.
besides the expected uses mentioned above, we analyzed the outcome of the model for off-class uses .
we examined the agreement between the anatomical therapeutic chemical  classification system codes of the drugs and the top mesh  disease hierarchy for the suggested indications.
for  <dig> drugs with only one atc code, we checked whether the model offered any indication for off-class use.
for 66% of the pairs, we found indications for an additional therapeutic class .
for instance, an antineoplastic agent , thalidomide, was suggested for the treatment of osteoporosis, which is a musculoskeletal disease.
this new therapeutic class for thalidomide was supported by earlier studies  <cit> .
we also compared our results with those generated by others.
yang and agarwal  <cit>  generated alternative indications for  <dig> drugs based on side effect profiles from sider and indication data from pharmgkb  <cit> .
of the  <dig> drugs,  <dig> were also used in our study, and for  <dig> of them , the two methods agreed on at least one indication.
of the  <dig> drugs whose indications were missing in sider, we found that only  <dig> were included in the study by yang and agarwal.
among these,  <dig> had at least one new indication consistent with our prediction.
in addition, sirota et al.
compared gene expression signatures for  <dig> diseases and  <dig> drugs and concluded that a reverse correlation between expression changes would help to identify candidate drugs for given diseases  <cit> .
they predicted valproic acid for the treatment of brain tumor and esophagus, lung, and colon cancers.
our model predicted its potential use for tumor and metastases, which has also been supported by an earlier study  <cit> .
side effects and indications provide a view to the mechanism of a drug, and by using lda we might be able to use this information to better understand the hidden relationship between a drug, its therapeutic uses, and the side effects it causes.
this information can then be used to identify new potential uses for drugs, and our model provides both a probability and rank order to assess these uses.
this approach shows significant promise for improving the understanding of identifying new uses for existing drugs as well as drug-topic-side effect relationships that could be used for adverse event prediction.
however, the latter task requires more exhaustive procedure and is beyond this study, because the number of perturbations needed is much higher.
finally, a number of caveats should be noted regarding the present study.
the most important limitation lies in the fact that we only have a partial knowledge about the phenome represented by sider.
while we are perturbing the drug-phenome matrix for recovering indications to observe the potential of partial knowledge, the degree of perturbation remains an open question.
this issue might be better addressed by conducting a study on a simulated data set where accuracy can be measured precisely.
secondly, the indication space  proposed in the study may limit the real world application of the methodology, since drug is is not available.
thirdly, lda imposes a multinomial distribution on the phenome and it may work better with higher accuracies when the real distribution is close multinomial distribution.
last but not least, our model does not allow sensitivity and specificity measurements due to the unknown nature of the data set.
CONCLUSIONS
in this work, we proposed an in silico framework for drug repositioning guided by the phenome that can narrow down candidate indications for given drugs and applied it to sider.
we treated the phenotypic information of a drug as a fingerprint and assumed it to be generated in a probabilistic fashion.
lda enabled us to retrieve conditional probabilities for new indications for given drugs.
evidence for findings retrieved by the model suggests new opportunities for repositioning, which supports the utility of this model in a systematic repositioning pipeline.

 the phenome, which can be defined as the comprehensive collection of phenotypic information, has been studied intensively to provide solutions for disease-centric biological problems. most studies in this area aim to demonstrate the correlation between genome and phenome, where various approaches have indicated the usefulness of the phenome to arrive at conclusions about diseases. for example, a global comparison of the human interactome and phenome through network alignment has also shown that genome-phenome associations can explain causalities on a larger scale. these studies demonstrate that the phenome can be mined by appropriate tools to discover new treatment opportunities for diseases. drug repositioning often involves a process of using knowledge accumulated about a drug during the de novo drug discovery process, clinical trial, and/or post-marketing surveillance to identify new therapeutic purposes other than the originally intended purpose. examining drugs with known safety profiles and pharmacokinetic properties can lead to new therapeutic indications more quickly and with less risk. the history of successful drug repositioning is comprised mostly of serendipitous findings, such as alternative indications for sildenafil and thalidomide, but a more systematic approach is advocated to explore the full benefit of this approach. interest in drug repositioning is increasing and has attracted researchers from academia, government, and industry, many of whom have developed in silico solutions to assist repositioning research. these in silico approaches demonstrate the potential of systematic study to improve drug repositioning efforts. in general, the reported studies can be classified as either disease-centric or drug-centric approaches. most of them used molecular, genomic, or phenotypic data. as an example of a molecular study for drug repositioning, keiser et al. measured the chemical similarities of drugs consisting of both us food and drug administration -approved and investigational drugs and linked the results to drug targets. they reported thousands of potential drug-target associations and experimentally validated of them that may add alternative therapeutic options for diseases. using genomic data, iorio et al. assessed drug similarity based on drug-elicited gene expression in cell lines with a network analysis approach. their work suggested that fasudil would be effective in the treatment of autophagy, which is a major process in cancer, and this was confirmed experimentally. in a separate study with genomic data, sirota et al. compared the gene expression profiling elicited by drugs and that profiled for diseases. they considered a drug effective for a disease if the expression profiles reversely matched. a supporting animal study verified that citemedine could be effective for lung cancer. in a follow up study using the same approach, they reported that anticonvulsant topiramate was effective in the treatment of inflammatory bowel disease. on the other hand, the use of the phenome to identify new therapeutic treatments has also been explored in the research community. for instance, campillos et al. hypothesized that drugs having common side effects can also treat the same disease and examined drug-drug pairs, of which nine were experimentally verified for alternative therapeutic uses. yang et al. also studied side effects to assess their associations with diseases through statistical tests. they further focused on the drugs that showed a particular side effect but was not mentioned with the strongly associated indication. current in silico methodologies in drug repositioning, including phenome-based techniques, mostly rely on drug-drug similarity measurements which can lead to guilt-by-association. in other words, the search space is often restricted to the most similar drug without taking full advantage of the information embedded in the entire dataset. we proposed that the phenome should be explored with a probabilistic generative model for a comprehensive analysis. such an analysis may reveal further links between drugs and diseases. one important consideration for use of the phenome data is its quality. although a drug’s phenome should be consistent within a specific population, the information recorded in some sources, may still remain partial. the missing information could be due to the lack of studies and resources to comprehensively and accurately record all the possible side effects and indications of a drug. given the challenges to gather a reliable phenome collection, sider stands as a well-credited data source with a room to exploit partial knowledge. in other words, some of alternative indications of a drug that are not yet captured in the existing phenome database are ready for discovery, which is the objective of this study. this study is built on two premises: the allocation of phenotypes in a phenome arising through both on-target and off-target methods follow a probabilistic distribution in which the therapeutic indications of drugs are embedded and the inherent probabilistic relationship of a drug’s phenome is insensitive to the incompletion of the phenome source. thus, a probabilistic graph model can be built using the partial phenome and used to identify repositioning opportunities. to build probabilistic associations between existing drugs and possible indications, we used a bayesian model, latent dirichlet allocation, which was primarily developed for topic modeling of text documents. with its probabilistic nature, lda can link documents and words through latent variables known as topics. lda achieves such relations by manipulating words, which are the only observed variables, in a bayesian setting where documents are assumed to be a weighted mixture of multiple topics. by analogy, we have extended this procedure to drugs and phenotypes since phenotypes are observable outcomes of drugs after they hit intended/unintended targets. in other words, this mode of action mimics like a latent process where therapeutic effects occur along with side effects due to unknown cellular mechanisms. since we cannot holistically observe the mechanism causing both effects, we may rely on hidden variables that play a role in the observed outcomes. therefore, phenotypes are said to be probabilistically distributed across drugs by hidden variables and the same distribution can be utilized to complete missing information. more specifically, these hidden variables can let us discover the potential links between drugs and phenotypes, specifically indications. lda has previously been applied in biological studies such as discovering relationships in pubmed articles, mining relational paths in the biomedical data, and grouping fda approved drugs based on their therapeutic uses and safety concerns. however, its ability to suggest potential associations has yet to be investigated for drug repositioning. in this study, we applied lda to the sider database to approximate the phenome distribution of drugs. to develop a criterion for suggesting new uses of drugs, we first observed the recovery potential of the model by predicting each “missing” indication from the phenome that was deliberately switched to unknown. in doing so, we took the advantage of not only remaining indication information, but also the whole side effect profile, which is another descriptive feature of a drug. then, we required a prediction to be both significant and ranked within the top-k positions, where k is equal to the number of known indications for a given drug. we refer to this last characteristic as being within the indication space of a drug. imposing the same criteria to our initial drug-phenome matrix without any perturbation, we further employed this procedure for drug repurposing. in other words, we attempted to make use of our partial knowledge about side effects and indications to systematically cut down the possible number of drug-indication pairs that can suggest alternative indications. finally, we both examined several suggested uses by supportive evidence in the literature and captured the original indications of some drugs whose indications were not recorded in sider.  as depicted in figure  this study consists of the following steps: building a drug-phenome matrix with the data profile obtained from sider; determining the optimal number of topics by using an information loss criterion to derive a lda model; determining the criteria of the model for application with a procedure that assumed the absence of each indication in a one-by-one fashion; applying lda on the drug-phenome matrix to get probabilities for empty cells, validating the approach by finding known indications mislabeled in sider; and predicting new indications for given drugs and validating them through various sources.figure overview of the study. a) integrating side-effects and indications to complete phenome; b) determining the number of topics by using information loss; c) hiding known indications one by one to see the recovery potential for decision criteria; d) developing the decision criteria considering recovered indications with significance; e-f) lda is applied to the drug-phenome matrix. observing the decision criteria, real indications were recovered for drugs without indication information and new indications suggested for remaining drugs. number of topics we computed the optimal number of topics for the drug-phenotype matrix by using an information loss criterion. when the objective function is minimized, the corresponding number of topics is considered to be the optimal. the information loss reached its minimum when the number of topics hit developing decision criteria as shown in figure 1c, a total of cases were tested to assess the recovery rate for the masked indications. specifically, an entry of “1” in the matrix was replaced by “0” one at a time, and then lda was conducted on the remaining entries and the conditional probability of the indication for the given drug p along with the position of the indication in a ranked list based on probabilities for all indications p were calculated. in order to develop a decision criterion before suggesting alternative indications, we considered the probability of a prediction against chance as well as its location depending on a drug’s is. first, as shown in figure 1d, we rejected predictions below random chance, which identified cases that correspond to bars above in figure  next, the cases were further analyzed against each drug’s is. in out of cases, the held indication was recovered correctly to be within the drug’s is, yielding an average success rate of 70% within the portion of pairs that has greater probability than the random chance.figure binned probabilities for known indications in cases. p stands for the probability of the indication in a drug-indication pair. each bar denotes the number of known pairs falling into the probability intervals. blue bars show the cases over random chance, and we have drug-indication pairs satisfying this condition. of note, the individual success rate varied on a drug’s is; the rate was lower in the case of drugs with fewer known indications. for example, we calculated the success rate by incrementally removing the drugs with the lower number of indications. an increasing success rate from 70% to 100% was observed for recovering indications. the results demonstrated that high rates were mostly associated with drugs that have a higher number of indications. retrieving original indications after developing the criteria, which gave a success rate of 70% for the masked indications, we ran lda on the drug-phenome matrix in order to discover new links between drugs and indications. this section reports findings for the retrieval of original indications and alternative indications for drug repositioning. the sider database has indication information for most drugs along with side effect profiles. however, of the drugs in the database were not associated with any indications, even though of them had known therapeutic uses. we suspected that these compounds should have multiple indications. therefore, we employed our model to suggest the missing indication. we relied on the same decision criteria, but assumed that the is =  which was the average number of indications in the data set. for each of the drugs, we examined the top indications whose probabilities were higher than the threshold. in order to verify drug-indication pairs, we reviewed the reports from the fda-approved drug labels, drugbank, and the scientific literature. we confirmed that drugs were shown to be effective for at least one indication suggested by the model. in fact, for of these, the model captured the original approved indications.table indications retrieved by the model findings supported by literature drug indication rank reference suggesting alternative indications for drug repositioning containing drugs with indications, sider provides a sparse representation of drugs and their indications, which implies that there are many unknown, but possible, drug-indication associations to be investigated. in that regard, our model worked as a screening technique that might help to reduce the number of candidates for further explorations. as a matter of fact, relying on the established criteria, our model brought up potential drug-indication pairs that correspond to % of the total number of possible pairs. for the suggested drug indication pairs, the relationship between indication space and p is illustrated in figure  the figure also provides the number of drug-indication pairs for the drugs, which were grouped based on the is bins they fall in. these two illustrations show that the number of pairs and probabilities for the pairs with lower is are greater. this observation implies that for the drugs with smaller is, the model can suggest alternative indications that are being highly probable and worth to investigate, although it was a challenging task to recover masked indications for the same drugs.figure binned suggested pairs based on is along with probabilities. x-axis denotes the number of indications and the bars represent the number of cases including drugs with that many indications. box plots summarize the probability distribution of suggested drug-indication pairs. the model offered new treatment options to be investigated for drugs for which two criteria mentioned earlier was satisfied. when the alternative indications were examined, some of the suggestions were confirmed. table  shows a partial list of these confirmations with supportive sources for the new indications that were ranked within the top among indications. for instance, amantadine is currently used as medication for influenza a virus and our model suggested alternative uses including the treatment of epilepsy. we conducted a literature search for this suggested treatment option and found that amantadine had been studied and had been beneficial in reducing seizures. additionally, atazanavir was suggested for use in hiv infection, which is an fda-approved indication but is not mentioned in sider.table verification of suggested uses through literature discussion experimentally evaluating alternative therapeutic uses of all marketed drugs is time-consuming and labor-intensive. thus, developing an in silico methodology for drug repositioning is a cost-effective way to move the field forward. in this study, we proposed an in silico framework which aims to elaborate the entire observed phenome of the available drugs by a probabilistic graphical model. using lda, we identified the probabilistic associations of drugs and phenotypes through a latent variable. under two established decision criteria, we applied the method to infer the probability of new indications for a given drug. the methodology quantifies the nature of a possible relationship that is not previously known. furthermore, it does not require any a priori information because of its unsupervised nature. another notable advantage of lda is its flexibility to link a drug with multiple latent variables through which drugs can be linked to various indications, including those that do not exist in sider. we first established the criteria for the use of lda on whole phenome by first assuming the absence of each known drug-indication pair and making predictions for that particular indication. for each case, we checked whether the probability ) was greater than random chance and the indication was ranked within the range of known indications for the given drug. since q =  is a deterministic threshold for all predictions, it is not allowed to vary with the success ratio that may lead to overfitting. indeed, its impact remains more unbiased with respect to higher thresholds. on the other hand, we primarily employed is as a parametric measure to determine a drug-specific degree of freedom, which enabled us to assess the validity of the model for known pairs and to make a fair comparison. the results show that success rate of the model mostly depends on the number of known indications, with higher rates of success for drugs with many known indications. this is likely because the greater amount of information increased our ability to identify hidden relationships. nonetheless, the model performed well even on those drugs with fewer indications, with a minimum of 70% success for drugs with at least one known indication. the capacity of the model to retrieve held-out indications demonstrates its applicability in real-world scenarios. thus, we made an attempt to retrieve indications for drugs that had been mistakenly omitted in the original dataset from sider. of the drugs with known therapeutic uses in this group, the model was able to identify an indication that had supporting evidence for of them; for of these the indication identified was the approved indication for the drug. the final goal of the model is to identify novel uses for existing drugs. this model identified drugs from sider with new potential indications for further investigations. similar to the earlier studies, we provided potential pairs that might require an exhaustive search for evidence to verify that the pairs were promising. therefore, we performed a co-occurrence search in pubmed for the drug-indication pairs and observed that 75% of the pairs were mentioned together in an abstract at least once. even though co-occurrence cannot be a proof of therapeutic effect alone, it implies the relatedness of many such pairs. then, since we could not go through the whole list, we made an effort to examine several of the proposed repositioning opportunities to determine if any supporting evidence for the indication existed. we examined the scientific literature, clinical trial data, and approved drug labels to find supportive evidence for the suggested use. the prediction of alternative indications for a drug summarized in table  is based on one bayesian measure, i.e., the conditional probability of a particular alternative use for a given drug ). the bayesian characteristics also allow ranking drugs for queried indications; drugs can be ordered based on the conditional probability p. using the same strategy, drugs falling within the top k positions hold the potential for treatment of a queried disease. one rare disease, cystic fibrosis, was queried for a candidate drug. our model suggested ceftazidime for the treatment option, which was indicated earlier in the literature, but was mentioned in neither drug labels nor sider. of note, the p measure failed to identify the alternative use of ceftazidime for cystic fibrosis, but such an association was uncovered by using the p measure. the results indicate the utility of both measures for revealing repositioning opportunities. among the drugs meeting our decision criteria was thalidomide, a well-known example of a repositioned drug. the model correctly identified the new indication of multiple myeloma for thalidomide. the model, however, is constrained in some respects by our choice of data source. although sider is intended as a comprehensive resource for side effects and indications, not all known indications are included, and these missing indications may affect the performance of the model. for example, buproprion is a well-known example of serendipitous drug repositioning. initially approved as an anti-depressant, clinical observation indicated its potential for smoking cessation. we were unable to identify this new indication using lda, because this indication does not appear for any drugs in sider. this is reasonable that because no drug in the dataset is associated with smoking cessation it is not possible to identify that as an association using only this data set. besides the expected uses mentioned above, we analyzed the outcome of the model for off-class uses. we examined the agreement between the anatomical therapeutic chemical classification system codes of the drugs and the top mesh disease hierarchy for the suggested indications. for drugs with only one atc code, we checked whether the model offered any indication for off-class use. for 66% of the pairs, we found indications for an additional therapeutic class. for instance, an antineoplastic agent, thalidomide, was suggested for the treatment of osteoporosis, which is a musculoskeletal disease. this new therapeutic class for thalidomide was supported by earlier studies. we also compared our results with those generated by others. yang and agarwal generated alternative indications for drugs based on side effect profiles from sider and indication data from pharmgkb. of the drugs, were also used in our study, and for of them, the two methods agreed on at least one indication. of the drugs whose indications were missing in sider, we found that only were included in the study by yang and agarwal. among these, had at least one new indication consistent with our prediction. in addition, sirota et al. compared gene expression signatures for diseases and drugs and concluded that a reverse correlation between expression changes would help to identify candidate drugs for given diseases. they predicted valproic acid for the treatment of brain tumor and esophagus, lung, and colon cancers. our model predicted its potential use for tumor and metastases, which has also been supported by an earlier study. side effects and indications provide a view to the mechanism of a drug, and by using lda we might be able to use this information to better understand the hidden relationship between a drug, its therapeutic uses, and the side effects it causes. this information can then be used to identify new potential uses for drugs, and our model provides both a probability and rank order to assess these uses. this approach shows significant promise for improving the understanding of identifying new uses for existing drugs as well as drug-topic-side effect relationships that could be used for adverse event prediction. however, the latter task requires more exhaustive procedure and is beyond this study, because the number of perturbations needed is much higher. finally, a number of caveats should be noted regarding the present study. the most important limitation lies in the fact that we only have a partial knowledge about the phenome represented by sider. while we are perturbing the drug-phenome matrix for recovering indications to observe the potential of partial knowledge, the degree of perturbation remains an open question. this issue might be better addressed by conducting a study on a simulated data set where accuracy can be measured precisely. secondly, the indication space proposed in the study may limit the real world application of the methodology, since drug is is not available. thirdly, lda imposes a multinomial distribution on the phenome and it may work better with higher accuracies when the real distribution is close multinomial distribution. last but not least, our model does not allow sensitivity and specificity measurements due to the unknown nature of the data set.  in this work, we proposed an in silico framework for drug repositioning guided by the phenome that can narrow down candidate indications for given drugs and applied it to sider. we treated the phenotypic information of a drug as a fingerprint and assumed it to be generated in a probabilistic fashion. lda enabled us to retrieve conditional probabilities for new indications for given drugs. evidence for findings retrieved by the model suggests new opportunities for repositioning, which supports the utility of this model in a systematic repositioning pipeline. 
the results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. the phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. in this regard, lda serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space. next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. correspondingly, we employed latent dirichlet allocation, which introduces latent variables to govern the phenome distribution. drug repositioningbayesian methodslatent dirichlet allocationdata miningphenomeside effectsindicationsissue-copyright-statement© the author 2014 we first developed a lda model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to “unknown” one at the time and then recovered based on the remaining drug-phenotype pairs. several of the suggested new uses can be supported with information from the scientific literature. for a comprehensive analysis of the phenome, we assumed that all phenotypes were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. for drugs that were present with their indication information, our model suggested alternative treatment options for further investigations.
 the phenome represents a distinct set of information in the human population. it has been explored particularly in its relationship with the genome to identify correlations for diseases. the phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. for a comprehensive analysis of the phenome, we assumed that all phenotypes were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. correspondingly, we employed latent dirichlet allocation, which introduces latent variables to govern the phenome distribution.  we developed our model on the phenome information in side effect resource. we first developed a lda model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to “unknown” one at the time and then recovered based on the remaining drug-phenotype pairs. of the probabilistically significant pairs, 70% was successfully recovered. next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. we were able to retrieve approved indications of drugs whose indications were not listed in sider. for drugs that were present with their indication information, our model suggested alternative treatment options for further investigations. several of the suggested new uses can be supported with information from the scientific literature.  the results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. in this regard, lda serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords drug repositioningbayesian methodslatent dirichlet allocationdata miningphenomeside effectsindicationsissue-copyright-statement© the author 2014 