BACKGROUND
ra is a chronic inheritable autoimmune disease characterized by inflammation and destruction of synovial joints.
ra affects up to  <dig> % of the population worldwide  <cit> .
the cause of ra remains unknown, with multiple genetic and environmental factors involved  <cit> .
currently, there is no drug that can cure ra or achieve sustained remission.
disease genetics can lead to the identification of novel drug treatments.
over the past decade, genome-wide association studies  have robustly identified genetic risk loci linked to many complex diseases, including  <dig> risk loci for ra  <cit> , and have provided valuable biological insights into many common diseases  <cit> .
several recent studies have indicated that disease genetics identified by gwas may lead to translational opportunities for drug discovery .
for example, a recent study showed that ra risk loci identified through meta-analysis of gwas data provided therapeutic opportunities for the repositioning of existing drugs for the treatment of ra  <cit> .
to capitalize on complex human disease genetics identified through gwas, the national center for advancing translational science  was established to use genomic information to determine whether drugs approved to treat one disease could be effective in treating others  <cit> .
however, significant challenges exist in directly translating disease-associated genetic variants identified by gwas into novel therapeutics  <cit> .
here, we propose an alternative drug discovery approach by combining lower-level disease genetics identified by gwas with higher-level drug treatment databases that we recently constructed .
we hypothesize that the genetic overlap among diseases often reflects pathophysiological overlap.
though the majority of such shared pathophysiological features may remain unknown , treatment insights from one disease may be used to inform our knowledge of others and potentiate their treatments.
instead of directly inferring drug targets from disease genetics as previous approaches , we use diseases that share high degrees of genetic commonalities with ra  as the starting point for discovering new drug treatments for ra .
for example, if a drug treats many ra-related diseases, then this drug is more likely to be a promising candidate to treat ra than a drug that treats few ra-related diseases.
compared to the traditional disease genetics-based or target-based drug discovery, we can put such inference into practice without precise knowledge of disease biology or drug mechanisms.
fig.
<dig> indirect disease genetics-based drug repositioning approach
computational drug repositioning approaches can be classified as either drug-based or disease-based  <cit> .
drug-based approaches leverage upon known drug molecular structures or functions such as chemical structure and properties, molecular docking, gene expression, drug treatments, and drug side effects .
it was recognized that drug screening based on existing drugs  might fail to identify new therapeutic mechanisms  <cit> .
on the other hand, disease-based approaches put less emphasis on existing drugs and focus more on disease mechanisms or interrelationships among diseases, therefore have potential in discovering truly innovative drugs.
disease-based approaches have used disease-related data ranging from genome  <cit>  to phenome .
while existing drug repositioning systems often used well-established computational or statistical methods, including regression/classification, machine learning, network analysis, and text mining  <cit> , they differ in the datasets included in the systems and how heterogeneous data are integrated.
the keys to our drug repositioning system include both the unique datasets included in the system as well as innovative approaches to integrating various disease- and drug-related data.
one of the key components of our system is four large-scale drug-disease treatment relationship knowledge bases  that we recently constructed from multiple heterogeneous and complementary data resources using advanced computational techniques including natural language processing, text mining and data mining .
the four treatkb include  <dig>  drug-disease treatment pairs extracted from fda drug labels,  <dig>  pairs extracted from the fda adverse event reporting system , a database supporting the fda’s post-marketing drug safety surveillance,  <dig>  pairs extracted from  <dig> million published biomedical literature abstracts, and  <dig>  pairs extracted from  <dig>  clinical trials.
all together, treatkb contains  <dig>  drug-disease treatment pairs for  <dig> drugs and  <dig>  diseases.
in addition, we applied a novel signal prioritization algorithm that we recently developed  <cit> , which first identifies diseases that are genetically related to ra and then prioritizes drugs based on the relevance of their associated diseases to ra.
methods
the experiment framework is depicted in fig.
<dig> and consists of four steps:  we constructed a genetic disease network  using disease-gene associations from gwas.
we developed a network-based ranking algorithm to find diseases that shared high degrees of genetic commonality with ra;  we analyzed disease classes that were highly associated with ra in order to evaluate the disease-ranking algorithm and to gain insight into ra-related diseases;  we developed a drug prioritization algorithm to systematically reposition drugs from ra-related diseases to treat ra.
we retrospectively validated the algorithm using  <dig> fda-approved ra drugs.
we evaluated our algorithm in novel predictions using  <dig> not-yet-approved ra drugs.
we compared our approach  to okada’s study  <cit>   in both retrospective validation and novel predictions; and  we examined drug classes that were highly enriched among top-ranked drug candidates.
fig.
<dig> the overall experiment flow chart
find and analyze ra-related diseases
construct genetic disease network
we constructed a gdn using disease-gene associations from the catalog of published genome-wide association studies from the us national human genome research institute , which is an exhaustive source containing the descriptions of disease- and trait-associated single nucleotide polymorphisms  from published gwas data  <cit> .
we obtained a total of  <dig>  disease/trait-gene pairs, representing  <dig> diseases/traits and  <dig>  genes.
on gdn, two diseases were connected if their associated genes overlapped.
the edge weights were determined by the cosine similarity coefficients of disease-associated genes  <cit> .
we also experimented other similarity measures such as jaccard similarity coefficient and overlap.
since some diseases do not share genes directly but their associated genes may interact or participate in the same pathways, we also investigated alternative approaches to connect diseases on the networks.
we connected two diseases if their associated genes  interact or participate in the same pathways using data from the string database  <cit> .
experimental results showed that connecting diseases based on cosine similarity of their associated genes performed best.
gdn comprised of  <dig> disease nodes and  <dig>  edges.
apply network-based ranking algorithm to find ra-related diseases from gdn
we have recently developed network-based ranking algorithms to prioritize genes for a given disease  <cit>  or to prioritize diseases for a given microbial metabolite  <cit> .
the iterative network-based ranking algorithm is defined as: pt+1=mpt+γp <dig>  wherein m is the column-normalized adjacency matrix of gdn, γ is a preset probability of restarting from the initial seed node , and pt is a vector in which the ith element holds the normalized ranking score of disease i at tth iteration.
the initial probability vector p <dig> contains only ra, with a probability of  <dig> .
the iterative ranking algorithm was run until it converges, meaning that the change between pt+ <dig> and pt is less than 10− <dig>  diseases are ranked according to values in the steady-state probability vector pt.
analyze ra-related diseases
to better understand top-ranked diseases as well as to test the network construction and ranking algorithms, we examined the distributions of disease classes among ra-related diseases at different ranking cutoffs.
we classified diseases based on the 10th revision of the international statistical classification of diseases and related health problems   <cit> .
the icd <dig> includes  <dig> highest-level disease classes  such as “neoplasms” and “diseases of the nervous system.” during our experiments, we found that only six disease classes were well represented in the gwas catalog: immune diseases, autoimmune diseases, cardiovascular diseases,metabolic disorders, mental or psychiatric disorders, and neoplasms.
the two disease classes: immune diseases and autoimmune diseases, served as positive controls because ra is an autoimmune disease  and is expected to be related to other immune diseases.
we retrieved a ranked list of diseases from gdn using ra.
we calculated the percentage of these six disease classes among ranked diseases at  <dig> different ranking cutoffs .
reposition drugs based on ra-related diseases
drug repositioning algorithm
we have recently developed a drug prioritization approach to systematically reposition drugs that treat ra-related diseases to treat ra  <cit> .
the algorithm is based on the assumption that if a drug treats many top-ranked ra-related diseases, it will rank higher than another drug that treats one or two lower-ranked diseases.
we first ranked drugs based on the number of ra-related diseases that they could treat as well as the ranking scores of these diseases.
the drug prioritization algorithm is defined as: rdrug=∑i=1nr_disease_i, wherein n is the number of ra-related diseases that a drug can treat and r_disease_i is the disease ranking score .
de novo validation using known ra drugs and comparison
we evaluated our algorithm using a total of  <dig> fda-approved ra drugs.
this list of drugs were manually compiled from fda drug label and several authoritative medical-related websites such as webmd.com and drugs.com.
our evaluation was a de-novo validation scheme, since ra and its associated drug treatment pairs were removed from the inputs to the repositioning algorithm.
the standard precision, recall, and f <dig> measures were used.
we compared our study to okada’s study  <cit>  in predicting both fda-approved ra drugs as well as novel drugs .
in okada’s study, the authors first prioritized  <dig> ra risk genes, from which a total of  <dig> drug target genes of approved ra drugs were identified.
these  <dig> drug target genes were then connected to  <dig> drugs.
among these  <dig> drugs,  <dig> were fda-approved ra drugs and  <dig> were novel predictions.
we could not compare our approach to one of the state-of-art drug repositioning systems, predict  <cit> , since it does not include ra.
predict used disease-disease and drug-drug similarities from multiple data resources to construct a classifier to determine treatment associations between  <dig> drugs and  <dig> diseases, the majority of which are mendelian diseases.
one of predict’s limitations is that it can only infer new connections between the  <dig> drugs and  <dig> diseases included in the system.
evaluate novel predications
the fact that a drug repositioning algorithm has worked well in ranking known ra drugs  does not imply that it will work equally well in novel drug prediction; therefore, evaluation of novel prediction capability is critical for any drug repositioning algorithm.
instead of manually searching literature or clinical trials for evidence supporting novel predictions, we automated this process using the drug-disease treatment knowledge bases that we constructed from over  <dig> million biomedical literature records and from  <dig>  clinical trials.
we extracted a total of  <dig> ra drugs from published biomedical literature, including  <dig> fda-approved ra drugs and  <dig> novel drugs.
we extracted a total of  <dig> ra drugs from clinical trials, including  <dig> fda-approved ra drugs and  <dig> novel drugs.
combining ra drugs from these two resources, we obtained a total of  <dig> novel ra drugs after fda-approved drugs were removed.
we evaluated the performance of our algorithm in novel prediction using these  <dig> drugs.
we calculated precisions, recalls, and f <dig> measures at different ranking cutoffs.
we compared our study to okada’s study in novel predictions using the same evaluation dataset.
analyze repositioned drug candidates
to better understand top-ranked repositioned drug candidates, we examined which classes of drugs  classification system  <cit> ) were enriched.
drug classes enriched among top-ranked drug candidates could provide insights into the underlying mechanisms of action of drug candidates within those classes.
the atc system consists of  <dig> first-level codes,  <dig> second-level codes,  <dig> third-level codes,  <dig> fourth-level codes, and  <dig> fifth-level codes, which are individual drugs.
we experimented the drug classification using different levels of atc codes and found that third level atc codes gave sufficient granularity.
we calculated percentages of drug classes associated with the top  <dig> % of ranked drugs and compared them to those for all drugs.
we identified drug classes that showed at least a 2-fold enrichment.
RESULTS
evaluate network construction and disease ranking algorithms and analyze ra-related diseases
we retrieved a ranked list of  <dig> diseases from the gdn using ra as the input.
two classes were highly enriched among top-ranked diseases: autoimmune diseases and immune diseases .
among top  <dig> % of retrieved diseases,  <dig>  % were autoimmune diseases, representing a significant  <dig> -fold enrichment as compared to the  <dig>  % among all retrieved diseases.
we also observed a significant enrichment for immune diseases.
among the top  <dig> % of diseases,  <dig>  % were immune diseases, representing a significant  <dig> -fold enrichment as compared to  <dig>  % among all retrieved diseases.
these results were expected because ra itself is an immune disease as well as an autoimmune disease.
these results demonstrate the validity of both network construction and disease ranking algorithms.
the other four disease classes were not significantly enriched.
fig.
<dig> disease class distribution among ra-related diseases at  <dig> ranking cutoffs
retrospective validation with  <dig> fda-approved ra drugs
drug repositioning using the combined treatkb has better performance than individual treatkbs
we validated our drug repositioning algorithm using  <dig> fda approved ra drugs.
since the drug treatment knowledge bases  were constructed from different data resources using different computational methods, we evaluated which treatkbs performs better in drug repositioning for ra.
we calculated recalls, mean, and median rankings of these fda-approved ra drugs when the four different treatkbs were used separately or combined .
when the treatkb derived from fda drug labeling  was used, we achieved a recall of  <dig> , an average ranking of  <dig>  %, and a median ranking of  <dig>  %.
we achieved significantly better rankings when the two treatkbs that were constructed from post-marketing faers  and from the biomedical literature  were used, respectively.
significantly, when the combined treatkb was used, we achieved a recall of  <dig>  , a mean ranking of  <dig>  %, and a median ranking of  <dig>  %.
the significantly improved recall and rankings demonstrate the critical importance of a comprehensive drug treatment knowledge base in drug repositioning tasks.
in comparison, such high rankings were not evident in our analysis for randomly selected fda-approved drugs .
as shown in table  <dig>  there is a significant difference between the median ranking of  <dig>  % and the mean ranking of  <dig>  %, demonstrating a skewed ranking distribution of these ra drugs.
fig.
<dig> shows that the rankings of ra drugs varied greatly from  <dig>  %  to  <dig>  % .
these results indicate that not all ra drugs can be discovered based on disease genetics.
fig.
<dig> rankings of  fda-approved ra drugs among  <dig> drugs.
the combined treatkb was used
combined
<dig>
<dig>  %
<dig>  %
the best performance was achieved when four treatkbs were combined
our approach has comparable overall performance but complementary precision and recall as compared to a “direct genetics-based” approach
as shown in fig.
<dig>  our drug repositioning algorithm proved effective in ranking fda-approved ra drugs at the top: the precision was  <dig>  for top  <dig> drugs , which represents a significant  <dig> -fold enrichment as compared to the  <dig>  for all  <dig> drugs .
the best overall performance was achieved at a cutoff of  <dig> % : a precision of  <dig> , a recall of  <dig> , and an f <dig> of  <dig>  were achieved.
fig.
<dig> the performance  of our algorithm at six different cutoffs  when evaluated using  <dig> fda-approved ra drugs.
for comparison, okada’s study is shown in blue
we compared our study to okada’s study in prioritizing known ra drugs.
when evaluated using the  <dig> fda-approved ra drugs, okada’s study achieved a recall of  <dig> , a precision of  <dig>  and an f <dig> of  <dig> .
our algorithm, at a  <dig> % cutoff , achieved a precision of  <dig> , a recall of  <dig> , and an f <dig> of  <dig>  .
while okada’s study achieved a higher precision, our study achieved a higher recall, indicating that these two approaches are largely complementary.
evaluation of novel predictions using  <dig> novel ra drugs
we evaluated our algorithm in novel prediction.
as shown in fig.
<dig>  our drug repositioning algorithm proved effective in prioritizing novel ra drugs, achieving a precision of  <dig>  for the top  <dig> drugs , which represents a significant  <dig> -fold enrichment as compared to the  <dig>  for all  <dig> drugs.
the best overall performance in novel prediction was achieved at a cutoff of  <dig> % : a precision of  <dig> , a recall of  <dig> , and an f <dig> of  <dig> .
fig.
<dig> the performance  of our algorithm at six different cutoffs  when evaluated using  <dig> novel ra drugs.
for comparison, okada’s study is shown in blue
okada’s study made a total of five novel predictions .
among these five drugs, two drugs  appeared in the evaluation dataset: both certolizumab pegol and tacrolimus currently are in active clinical trials.
therefore, okada’s study has a precision of  <dig> , a recall of  <dig>  , and an overall f <dig> of  <dig>  when evaluated using the set of  <dig> drugs .
the best performance for our system was achieved at a cutoff of  <dig> % : a precision of  <dig> , a recall of  <dig> , and an f <dig> of  <dig> , representing a 47-fold increase in f <dig> as compared to the f <dig> of  <dig>  in okada’s study.
the drug tacrolimus from okada’s study was ranked at top  <dig>  % position  by our algorithm.
in summary, we show that our repositioning strategy has comparable overall performance  to okada’s study in retrospective validation using the fda-approved ra drugs.
however, our algorithm has performed significantly better in finding novel ra drugs, and therefore has greater potential in the task of discovering innovative drug treatments for ra.
drug categories for top-ranked drug candidates offer insight into the underlying mechanisms of drug actionsfig.
<dig> top  <dig> third-level atc codes for top  <dig> % repositioned drug candidates that show at least  <dig> % enrichment as compared to percentages of the same atc codes for all drugs.
both immune- and inflammation-related atc codes were highlighted as red
discussion and
CONCLUSIONS
in this study, we prioritized a total of  <dig> drugs in terms of their relevance in the potential treatment of ra.
while previous studies demonstrated that directly linking disease-associated genes from gwas data to drug targets can lead to novel drug discovery, our study provides an alternative strategy to capitalize on complex human  <dig> genetics and comprehensive drug treatment data for other diseases for the discovery of innovative drug treatments for ra.
our algorithm retrieved  <dig> out of  <dig> fda-approved ra drugs and ranked those drugs highly, demonstrating the validity of our approach.
in addition, our algorithm proved effective in predicting innovative drugs for ra.
nonetheless, our study can be significantly improved upon in the future.
our current study was restricted by the limited number of diseases  in the gwas catalog, even though treatkb includes  <dig>  diseases.
with new studies being continually added to the gwas catalog, as well as new disease-gene associations increasingly being revealed by next-generation sequencing studies, additional drug repositioning opportunities will arise through human genetic analysis.
additionally, disease genetics from rare mendelian disorders represents another valuable source of novel drug targets and may lead to surprising and novel drug discovery opportunities  <cit> .
another rich resource for knowledge of human disease genetics is computation-based candidate disease gene prediction.
computational disease gene prediction aims to find new disease-gene associations through integrative computational analysis of known data of diseases, genes, functional protein interactions, gene expression, and the biomedical literature, among many others  <cit> .
we recently showed that computationally-predicted disease genetics can lead to novel drug discovery  <cit> .
in the future, we will combine comprehensive disease genetics data from all three of the above-described sources with a novel computational strategy to find new drug treatments for ra.
this study focuses on disease genetics-based drug repositioning.
additional invaluable resources such as other disease-related data  and drug-related data  can be incorporated into the currently proposed algorithm to further improve performance.
integrating and reasoning over such complex biological data poses a significant challenge that bears future investigation.

 ra is a chronic inheritable autoimmune disease characterized by inflammation and destruction of synovial joints. ra affects up to % of the population worldwide. the cause of ra remains unknown, with multiple genetic and environmental factors involved. currently, there is no drug that can cure ra or achieve sustained remission. disease genetics can lead to the identification of novel drug treatments. over the past decade, genome-wide association studies have robustly identified genetic risk loci linked to many complex diseases, including risk loci for ra, and have provided valuable biological insights into many common diseases. several recent studies have indicated that disease genetics identified by gwas may lead to translational opportunities for drug discovery. for example, a recent study showed that ra risk loci identified through meta-analysis of gwas data provided therapeutic opportunities for the repositioning of existing drugs for the treatment of ra. to capitalize on complex human disease genetics identified through gwas, the national center for advancing translational science was established to use genomic information to determine whether drugs approved to treat one disease could be effective in treating others. however, significant challenges exist in directly translating disease-associated genetic variants identified by gwas into novel therapeutics. here, we propose an alternative drug discovery approach by combining lower-level disease genetics identified by gwas with higher-level drug treatment databases that we recently constructed. we hypothesize that the genetic overlap among diseases often reflects pathophysiological overlap. though the majority of such shared pathophysiological features may remain unknown, treatment insights from one disease may be used to inform our knowledge of others and potentiate their treatments. instead of directly inferring drug targets from disease genetics as previous approaches, we use diseases that share high degrees of genetic commonalities with ra as the starting point for discovering new drug treatments for ra. for example, if a drug treats many ra-related diseases, then this drug is more likely to be a promising candidate to treat ra than a drug that treats few ra-related diseases. compared to the traditional disease genetics-based or target-based drug discovery, we can put such inference into practice without precise knowledge of disease biology or drug mechanisms. fig. indirect disease genetics-based drug repositioning approach computational drug repositioning approaches can be classified as either drug-based or disease-based. drug-based approaches leverage upon known drug molecular structures or functions such as chemical structure and properties, molecular docking, gene expression, drug treatments, and drug side effects. it was recognized that drug screening based on existing drugs might fail to identify new therapeutic mechanisms. on the other hand, disease-based approaches put less emphasis on existing drugs and focus more on disease mechanisms or interrelationships among diseases, therefore have potential in discovering truly innovative drugs. disease-based approaches have used disease-related data ranging from genome to phenome. while existing drug repositioning systems often used well-established computational or statistical methods, including regression/classification, machine learning, network analysis, and text mining, they differ in the datasets included in the systems and how heterogeneous data are integrated. the keys to our drug repositioning system include both the unique datasets included in the system as well as innovative approaches to integrating various disease- and drug-related data. one of the key components of our system is four large-scale drug-disease treatment relationship knowledge bases that we recently constructed from multiple heterogeneous and complementary data resources using advanced computational techniques including natural language processing, text mining and data mining. the four treatkb include drug-disease treatment pairs extracted from fda drug labels, pairs extracted from the fda adverse event reporting system, a database supporting the fda’s post-marketing drug safety surveillance, pairs extracted from million published biomedical literature abstracts, and pairs extracted from clinical trials. all together, treatkb contains drug-disease treatment pairs for drugs and diseases. in addition, we applied a novel signal prioritization algorithm that we recently developed, which first identifies diseases that are genetically related to ra and then prioritizes drugs based on the relevance of their associated diseases to ra. methods the experiment framework is depicted in fig. and consists of four steps: we constructed a genetic disease network using disease-gene associations from gwas. we developed a network-based ranking algorithm to find diseases that shared high degrees of genetic commonality with ra; we analyzed disease classes that were highly associated with ra in order to evaluate the disease-ranking algorithm and to gain insight into ra-related diseases; we developed a drug prioritization algorithm to systematically reposition drugs from ra-related diseases to treat ra. we retrospectively validated the algorithm using fda-approved ra drugs. we evaluated our algorithm in novel predictions using not-yet-approved ra drugs. we compared our approach to okada’s study in both retrospective validation and novel predictions; and we examined drug classes that were highly enriched among top-ranked drug candidates. fig. the overall experiment flow chart find and analyze ra-related diseases construct genetic disease network we constructed a gdn using disease-gene associations from the catalog of published genome-wide association studies from the us national human genome research institute, which is an exhaustive source containing the descriptions of disease- and trait-associated single nucleotide polymorphisms from published gwas data. we obtained a total of disease/trait-gene pairs, representing diseases/traits and genes. on gdn, two diseases were connected if their associated genes overlapped. the edge weights were determined by the cosine similarity coefficients of disease-associated genes. we also experimented other similarity measures such as jaccard similarity coefficient and overlap. since some diseases do not share genes directly but their associated genes may interact or participate in the same pathways, we also investigated alternative approaches to connect diseases on the networks. we connected two diseases if their associated genes interact or participate in the same pathways using data from the string database. experimental results showed that connecting diseases based on cosine similarity of their associated genes performed best. gdn comprised of disease nodes and edges. apply network-based ranking algorithm to find ra-related diseases from gdn we have recently developed network-based ranking algorithms to prioritize genes for a given disease or to prioritize diseases for a given microbial metabolite. the iterative network-based ranking algorithm is defined as: pt+1=mpt+γp wherein m is the column-normalized adjacency matrix of gdn, γ is a preset probability of restarting from the initial seed node, and pt is a vector in which the ith element holds the normalized ranking score of disease i at tth iteration. the initial probability vector p contains only ra, with a probability of. the iterative ranking algorithm was run until it converges, meaning that the change between pt+ and pt is less than 10− diseases are ranked according to values in the steady-state probability vector pt. analyze ra-related diseases to better understand top-ranked diseases as well as to test the network construction and ranking algorithms, we examined the distributions of disease classes among ra-related diseases at different ranking cutoffs. we classified diseases based on the 10th revision of the international statistical classification of diseases and related health problems. the icd includes highest-level disease classes such as “neoplasms” and “diseases of the nervous system.” during our experiments, we found that only six disease classes were well represented in the gwas catalog: immune diseases, autoimmune diseases, cardiovascular diseases,metabolic disorders, mental or psychiatric disorders, and neoplasms. the two disease classes: immune diseases and autoimmune diseases, served as positive controls because ra is an autoimmune disease and is expected to be related to other immune diseases. we retrieved a ranked list of diseases from gdn using ra. we calculated the percentage of these six disease classes among ranked diseases at different ranking cutoffs. reposition drugs based on ra-related diseases drug repositioning algorithm we have recently developed a drug prioritization approach to systematically reposition drugs that treat ra-related diseases to treat ra. the algorithm is based on the assumption that if a drug treats many top-ranked ra-related diseases, it will rank higher than another drug that treats one or two lower-ranked diseases. we first ranked drugs based on the number of ra-related diseases that they could treat as well as the ranking scores of these diseases. the drug prioritization algorithm is defined as: rdrug=∑i=1nr_disease_i, wherein n is the number of ra-related diseases that a drug can treat and r_disease_i is the disease ranking score. de novo validation using known ra drugs and comparison we evaluated our algorithm using a total of fda-approved ra drugs. this list of drugs were manually compiled from fda drug label and several authoritative medical-related websites such as webmd.com and drugs.com. our evaluation was a de-novo validation scheme, since ra and its associated drug treatment pairs were removed from the inputs to the repositioning algorithm. the standard precision, recall, and f measures were used. we compared our study to okada’s study in predicting both fda-approved ra drugs as well as novel drugs. in okada’s study, the authors first prioritized ra risk genes, from which a total of drug target genes of approved ra drugs were identified. these drug target genes were then connected to drugs. among these drugs, were fda-approved ra drugs and were novel predictions. we could not compare our approach to one of the state-of-art drug repositioning systems, predict, since it does not include ra. predict used disease-disease and drug-drug similarities from multiple data resources to construct a classifier to determine treatment associations between drugs and diseases, the majority of which are mendelian diseases. one of predict’s limitations is that it can only infer new connections between the drugs and diseases included in the system. evaluate novel predications the fact that a drug repositioning algorithm has worked well in ranking known ra drugs does not imply that it will work equally well in novel drug prediction; therefore, evaluation of novel prediction capability is critical for any drug repositioning algorithm. instead of manually searching literature or clinical trials for evidence supporting novel predictions, we automated this process using the drug-disease treatment knowledge bases that we constructed from over million biomedical literature records and from clinical trials. we extracted a total of ra drugs from published biomedical literature, including fda-approved ra drugs and novel drugs. we extracted a total of ra drugs from clinical trials, including fda-approved ra drugs and novel drugs. combining ra drugs from these two resources, we obtained a total of novel ra drugs after fda-approved drugs were removed. we evaluated the performance of our algorithm in novel prediction using these drugs. we calculated precisions, recalls, and f measures at different ranking cutoffs. we compared our study to okada’s study in novel predictions using the same evaluation dataset. analyze repositioned drug candidates to better understand top-ranked repositioned drug candidates, we examined which classes of drugs classification system ) were enriched. drug classes enriched among top-ranked drug candidates could provide insights into the underlying mechanisms of action of drug candidates within those classes. the atc system consists of first-level codes, second-level codes, third-level codes, fourth-level codes, and fifth-level codes, which are individual drugs. we experimented the drug classification using different levels of atc codes and found that third level atc codes gave sufficient granularity. we calculated percentages of drug classes associated with the top % of ranked drugs and compared them to those for all drugs. we identified drug classes that showed at least a 2-fold enrichment.  evaluate network construction and disease ranking algorithms and analyze ra-related diseases we retrieved a ranked list of diseases from the gdn using ra as the input. two classes were highly enriched among top-ranked diseases: autoimmune diseases and immune diseases. among top % of retrieved diseases, % were autoimmune diseases, representing a significant -fold enrichment as compared to the % among all retrieved diseases. we also observed a significant enrichment for immune diseases. among the top % of diseases, % were immune diseases, representing a significant -fold enrichment as compared to % among all retrieved diseases. these results were expected because ra itself is an immune disease as well as an autoimmune disease. these results demonstrate the validity of both network construction and disease ranking algorithms. the other four disease classes were not significantly enriched. fig. disease class distribution among ra-related diseases at ranking cutoffs retrospective validation with fda-approved ra drugs drug repositioning using the combined treatkb has better performance than individual treatkbs we validated our drug repositioning algorithm using fda approved ra drugs. since the drug treatment knowledge bases were constructed from different data resources using different computational methods, we evaluated which treatkbs performs better in drug repositioning for ra. we calculated recalls, mean, and median rankings of these fda-approved ra drugs when the four different treatkbs were used separately or combined. when the treatkb derived from fda drug labeling was used, we achieved a recall of, an average ranking of %, and a median ranking of %. we achieved significantly better rankings when the two treatkbs that were constructed from post-marketing faers and from the biomedical literature were used, respectively. significantly, when the combined treatkb was used, we achieved a recall of, a mean ranking of %, and a median ranking of %. the significantly improved recall and rankings demonstrate the critical importance of a comprehensive drug treatment knowledge base in drug repositioning tasks. in comparison, such high rankings were not evident in our analysis for randomly selected fda-approved drugs. as shown in table there is a significant difference between the median ranking of % and the mean ranking of %, demonstrating a skewed ranking distribution of these ra drugs. fig. shows that the rankings of ra drugs varied greatly from % to %. these results indicate that not all ra drugs can be discovered based on disease genetics. fig. rankings of fda-approved ra drugs among drugs. the combined treatkb was used combined % % the best performance was achieved when four treatkbs were combined our approach has comparable overall performance but complementary precision and recall as compared to a “direct genetics-based” approach as shown in fig. our drug repositioning algorithm proved effective in ranking fda-approved ra drugs at the top: the precision was for top drugs, which represents a significant -fold enrichment as compared to the for all drugs. the best overall performance was achieved at a cutoff of %: a precision of, a recall of, and an f of were achieved. fig. the performance of our algorithm at six different cutoffs when evaluated using fda-approved ra drugs. for comparison, okada’s study is shown in blue we compared our study to okada’s study in prioritizing known ra drugs. when evaluated using the fda-approved ra drugs, okada’s study achieved a recall of, a precision of and an f of. our algorithm, at a % cutoff, achieved a precision of, a recall of, and an f of. while okada’s study achieved a higher precision, our study achieved a higher recall, indicating that these two approaches are largely complementary. evaluation of novel predictions using novel ra drugs we evaluated our algorithm in novel prediction. as shown in fig. our drug repositioning algorithm proved effective in prioritizing novel ra drugs, achieving a precision of for the top drugs, which represents a significant -fold enrichment as compared to the for all drugs. the best overall performance in novel prediction was achieved at a cutoff of %: a precision of, a recall of, and an f of. fig. the performance of our algorithm at six different cutoffs when evaluated using novel ra drugs. for comparison, okada’s study is shown in blue okada’s study made a total of five novel predictions. among these five drugs, two drugs appeared in the evaluation dataset: both certolizumab pegol and tacrolimus currently are in active clinical trials. therefore, okada’s study has a precision of, a recall of, and an overall f of when evaluated using the set of drugs. the best performance for our system was achieved at a cutoff of %: a precision of, a recall of, and an f of, representing a 47-fold increase in f as compared to the f of in okada’s study. the drug tacrolimus from okada’s study was ranked at top % position by our algorithm. in summary, we show that our repositioning strategy has comparable overall performance to okada’s study in retrospective validation using the fda-approved ra drugs. however, our algorithm has performed significantly better in finding novel ra drugs, and therefore has greater potential in the task of discovering innovative drug treatments for ra. drug categories for top-ranked drug candidates offer insight into the underlying mechanisms of drug actionsfig. top third-level atc codes for top % repositioned drug candidates that show at least % enrichment as compared to percentages of the same atc codes for all drugs. both immune- and inflammation-related atc codes were highlighted as red discussion and  in this study, we prioritized a total of drugs in terms of their relevance in the potential treatment of ra. while previous studies demonstrated that directly linking disease-associated genes from gwas data to drug targets can lead to novel drug discovery, our study provides an alternative strategy to capitalize on complex human genetics and comprehensive drug treatment data for other diseases for the discovery of innovative drug treatments for ra. our algorithm retrieved out of fda-approved ra drugs and ranked those drugs highly, demonstrating the validity of our approach. in addition, our algorithm proved effective in predicting innovative drugs for ra. nonetheless, our study can be significantly improved upon in the future. our current study was restricted by the limited number of diseases in the gwas catalog, even though treatkb includes diseases. with new studies being continually added to the gwas catalog, as well as new disease-gene associations increasingly being revealed by next-generation sequencing studies, additional drug repositioning opportunities will arise through human genetic analysis. additionally, disease genetics from rare mendelian disorders represents another valuable source of novel drug targets and may lead to surprising and novel drug discovery opportunities. another rich resource for knowledge of human disease genetics is computation-based candidate disease gene prediction. computational disease gene prediction aims to find new disease-gene associations through integrative computational analysis of known data of diseases, genes, functional protein interactions, gene expression, and the biomedical literature, among many others. we recently showed that computationally-predicted disease genetics can lead to novel drug discovery. in the future, we will combine comprehensive disease genetics data from all three of the above-described sources with a novel computational strategy to find new drug treatments for ra. this study focuses on disease genetics-based drug repositioning. additional invaluable resources such as other disease-related data and drug-related data can be incorporated into the currently proposed algorithm to further improve performance. integrating and reasoning over such complex biological data poses a significant challenge that bears future investigation. 
we then developed a drug prioritization algorithm to reposition drugs from ra-related diseases to treat ra. when compared to a study that used gwas data to directly connect ra-associated genes to drug targets, our algorithm achieved a comparable overall performance, but complementary precision and recall in retrospective validation. we first constructed a genetic disease network using disease genetics data from genome-wide association studies. the unknown cause of the disease represents a significant challenge in the drug development. ra affects up to % of the population worldwide. our approach performed significantly better in novel predictions when evaluated using not-yet-fda-approved ra drugs. currently, there are no drugs that can cure ra or achieve sustained remission. our algorithm found of the fda-approved ra drugs and ranked them highly, demonstrating the validity of our strategy. rheumatoid arthritis is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. in this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between ra and other genetic diseases as well as a large amount of higher-level drug treatment data. we developed a network-based ranking algorithm to prioritize diseases genetically-related to ra. in summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating ra.
 rheumatoid arthritis is a chronic autoimmune disease characterized by inflammation and destruction of synovial joints. ra affects up to % of the population worldwide. currently, there are no drugs that can cure ra or achieve sustained remission. the unknown cause of the disease represents a significant challenge in the drug development. in this study, we address this challenge by proposing an alternative drug discovery approach that integrates and reasons over genetic interrelationships between ra and other genetic diseases as well as a large amount of higher-level drug treatment data. we first constructed a genetic disease network using disease genetics data from genome-wide association studies. we developed a network-based ranking algorithm to prioritize diseases genetically-related to ra. we then developed a drug prioritization algorithm to reposition drugs from ra-related diseases to treat ra.  our algorithm found of the fda-approved ra drugs and ranked them highly, demonstrating the validity of our strategy. when compared to a study that used gwas data to directly connect ra-associated genes to drug targets, our algorithm achieved a comparable overall performance, but complementary precision and recall in retrospective validation. our approach performed significantly better in novel predictions when evaluated using not-yet-fda-approved ra drugs.  in summary, although the fundamental pathophysiological mechanisms remain uncharacterized, our proposed computation-based drug discovery approach to analyzing genetic and treatment interrelationships among thousands of diseases and drugs can facilitate the discovery of innovative drugs for treating ra. keywords systems biologynetwork medicinedrug discoveryrheumatoid arthritisdisease genomicsthe international conference on intelligent biology and medicine indianapolis, in, usa 13- november http://watson.compbio.iupui.edu/yunliu/icibm/issue-copyright-statement© the author 2016 