Rephetio: Repurposing drugs on a hetnet
The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes the Rephetio Project to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated in two external datasets, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 36 community members.
The cost of developing a new therapeutic drug has been estimated at 1.4 billion dollars , the process typically takes 15 years from lead compound to market , and the likelihood of success is stunningly low . Strikingly, the costs have been doubling every 9 years since 1970, a sort of inverse Moore's law, which is far from an optimal strategy from both a business and public health perspective . Drug repurposing — identifying novel uses for existing therapeutics — can drastically reduce the duration, failure rates, and costs of approval . These benefits stem from the rich preexisting information on approved drugs, including extensive toxicology profiling performed during development, preclinical models, clinical trials, and postmarketing surveillance.
Drug repurposing is poised to become more efficient as mining of electronic health records (EHRs) to retrospectively assess the effect of drugs gains feasibility [6, 7, 8, 9]. However, systematic approaches to repurpose drugs based on mining EHRs alone will likely lack power due to multiple testing. Similar to the approach followed to increase the power of genome-wide association studies (GWAS) [10, 11], integration of biological knowledge to prioritize drug repurposing will help overcome limited EHR sample size and data quality.
In addition to repurposing, several other paradigm shifts in drug development have been proposed to improve efficiency. Since small molecules tend to bind to many targets, polypharmacology aims to find synergy in the multiple effects of a drug . Network pharmacology assumes diseases consist of a multitude of molecular alterations resulting in a robust disease state. Network pharmacology seeks to uncover multiple points of intervention into a specific pathophysiological state that together rehabilitate an otherwise resilient disease process [13, 14]. Although target-centric drug discovery has dominated the field for decades, phenotypic screens have more recently resulted in a comparatively higher number of first-in-class small molecules . Recent technological advances have enabled a new paradigm in which mid- to high-throughput assessment of intermediate phenotypes, such as the molecular response to drugs, is replacing the classic target discovery approach [16, 17, 18]. Furthermore, integration of multiple channels of evidence, particularly diverse types of data, can overcome the limitations and weak performance inherent to data of a single domain . Modern computational approaches offer a convenient platform to tie these developments together as the reduced cost and increased velocity of in silico experimentation massively lowers the barriers to entry and price of failure [20, 21].
Hetnets (short for heterogeneous networks) are networks with multiple types of nodes and relationships. They offer an intuitive, versatile and powerful structure for data integration. In this study, we developed a hetnet (Hetionet v1.0) by integrating knowledge and experimental findings from decades of biomedical research spanning millions of publications. We adapted an algorithm originally developed for social network analysis and applied it to Hetionet v1.0 to identify patterns of efficacy and predict new uses for drugs. The algorithm performs edge prediction through a machine learning framework that accommodates the breadth and depth of information contained in Hetionet v1.0 [22, 23]. Our approach represents an in silico implementation of network pharmacology that natively incorporates polypharmacology and high-throughput phenotypic screening.
One fundamental characteristic of our method is that it learns and evaluates itself on existing medical indications (i.e. a "gold standard"). Next, we introduce previous approaches that also performed comprehensive evaluation on existing treatments. A 2011 study, named PREDICT, compiled 1,933 treatments between 593 drugs and 313 diseases . Starting from the premise that similar drugs treat similar diseases, PREDICT trained a classifier that incorporates 5 types of drug-drug and 2 types of disease-disease similarity. A 2014 study compiled 890 treatments between 152 drugs and 145 diseases with transcriptional signatures . The authors found that compounds triggering an opposing transcriptional response to the disease were more likely to be treatments, although this effect was weak and limited to cancers. A 2016 study compiled 402 treatments between 238 drugs and 78 diseases and used a single proximity score — the average shortest path distance between a drug's targets and disease's associated proteins on the interactome — as a classifier .
We build on these successes by creating a framework for incorporating the effects of any biological relationship into the prediction of whether a drug treats a disease. By doing this, we were able to capture a multitude of effects that have been suggested as influential for drug repurposing including drug-drug similarity [24, 27], disease-disease similarity [24, 28], transcriptional signatures [25, 29, 17, 30, 18], protein interactions , genetic association [31, 32], drug side effects [33, 34], disease symptoms , and molecular pathways . Our ability to create such an integrative model of drug efficacy relies on the hetnet data structure to unite diverse information. On Hetionet v1.0, our algorithm learns which types of compound–disease paths discriminate treatments from non-treatments in order to predict the probability that a compound treats a disease.
We refer to this study as Project Rephetio (pronounced as rep-het-ee-oh). Both Rephetio and Hetionet are portmanteaus combining the words repurpose, heterogeneous, and network with the URL het.io.
We obtained and integrated data from 29 publicly available resources to create Hetionet v1.0 (Figure 1). The hetnet contains 47,031 nodes of 11 types (Table 1) and 2,250,197 relationships of 24 types (Table 2). The nodes consist of 1,552 small molecule compounds and 137 complex diseases, as well as genes, anatomies, pathways, biological processes, molecular functions, cellular components, perturbations, pharmacologic classes, drug side effects, and disease symptoms. The edges represent relationships between these nodes and encompass the collective knowledge produced by millions of studies over the last half century.
Figure 1. Hetionet v1.0
A) The metagraph, a schema of the network types. B) The hetnet visualized. Nodes are drawn as dots and laid out orbitally, thus forming circles. Edges are colored by type. C) Metapath counts by path length. The number of different types of paths of a given length that connect two node types is shown. For example, the top-left tile in the Length 1 panel denotes that Anatomy nodes are not connected to themselves (i.e. no edges connect nodes of this type between themselves). However, the bottom-left tile of the Length 4 panel denotes that 88 types of length-four paths connect Symptom to Anatomy nodes.
For example, Compound–binds–Gene edges represent when a compound binds to a protein encoded by a gene. This information has been extracted from the literature by human curators and compiled into databases such as DrugBank, ChEMBL, DrugCentral, and BindingDB. We combined these databases to create 11,571 binding edges between 1,389 compounds and 1,689 genes. These edges were compiled from 10,646 distinct publications, which Hetionet binding edges reference as an attribute. Binding edges represent a comprehensive catalog constructed from low throughput experimentation. However, we also integrated findings from high throughput technologies — many of which have only recently become available. For example, we generated consensus transcriptional signatures for compounds in LINCS L1000 and diseases in STARGEO.
Table 1. Metanodes
Hetionet v1.0 includes 11 node types (metanodes). For each metanode, this table shows the abbreviation, number of nodes, number of nodes without any edges, and the number of metaedges connecting the metanode.
Table 2. Metaedges
Hetionet v1.0 contains 24 edge types (metaedges). For each metaedge, the table reports the abbreviation, the number of edges, the number of source nodes connected by the edges, and the number of target nodes connected by the edges. Note that all metaedges besides Gene→regulates→Gene are undirected.
While Hetionet v1.0 is ideally suited for drug repurposing, the network has broader biological applicability. For example, we have prototyped queries for a) identifying drugs that target a specific pathway, b) identifying biological processes involved in a specific disease, c) identifying the drug targets responsible for causing a specific side effect, and d) identifying anatomies with transcriptional relevance for a specific disease . Each of these queries was simple to write and took less than a second to run on our publicly available Hetionet Browser. While it is possible that existing services provide much of the aforementioned functionality, they offer less versatility. Hetionet differentiates itself in its ability to flexibly query across multiple domains of information. As a proof of concept, we enhanced the biological process query (b), which identified processes that were enriched for disease-associated genes, using multiple sclerosis (MS) as an example disease. The enhanced query identified genes that interact with MS GWAS-genes. However, interacting genes were discarded unless they were upregulated in an MS-related anatomy (i.e. anatomical structure, e.g. organ or tissue). Then relevant biological processes were identified. Thus, the single query spanned 4 node and 5 relationship types. Furthermore, the portion of the query to identify paths meeting the above specification required only four lines of Cypher code.
The integrative potential of Hetionet v1.0 is reflected by its connectivity. Among the 11 metanodes, there are 66 possible source–target pairs. However, only 11 of them have at least one direct connection. In contrast, for paths of length 2, 50 pairs have connectivity (paths types that start on the source node type and end on the target node type, see Figure 1C). At length 3, all 66 pairs are connected. At length 4, the source–target pair with the fewest types of connectivity (Side Effect to Symptom) has 13 metapaths, while the pair with the most connectivity types (Gene to Gene) has 3,542 pairs. This high level of connectivity across a diversity of biomedical entities forms the foundation for automated translation of knowledge into biomedical insight.
Hetionet v1.0 is accessible via a Neo4j Browser at https://neo4j.het.io. This public Neo4j instance provides users an installation-free method to query and visualize the network. The Browser contains a tutorial guide as well as guides with the details of each Project Rephetio prediction. Hetionet v1.0 is also available for download in JSON, Neo4j, and TSV formats. The JSON and Neo4j database formats include node and edge properties — such as URLs, source and license information, and confidence scores — and are thus recommended.
Systematic mechanisms of efficacy
One aim of Project Rephetio was to systematically evaluate how drugs exert their therapeutic potential. To address this question, we compiled a gold standard of 755 disease-modifying indications, which form the Compound–treats–Disease edges in Hetionet v1.0. Next, we identified types of paths (metapaths) that occurred more frequently between treatments than non-treatments (any compound–disease pair that is not a treatment). The advantage of this approach is that metapaths naturally correspond to mechanisms of pharmacological efficacy. For example, the Compound–binds–Gene–associates–Disease (CbGaD) metapath identifies when a drug binds to a protein corresponding to a gene involved in the disease.
We evaluated all 1,206 metapaths that traverse from compound to disease and have length of 2–4 (Figure 2A). To control for the different degrees of nodes, we used the degree-weighted path count (DWPC) — which downweights paths going through highly-connected nodes  — to assess path prevalence. In addition, we compared the performance of each metapath to a baseline computed from permuted networks. Hetnet permutation preserves node degree while eliminating edge specificity, allowing us to isolate the portion of unpermuted metapath performance resulting from actual network paths. We refer to the permutation-adjusted performance measure as Δ AUROC.
Figure 2. Performance by type and model coefficients
A) The performance of the DWPCs for 1,206 metapaths, organized by their composing metaedges. The larger dots represent metapaths that were significantly affected by permutation (false discovery rate < 5%). Metaedges are ordered by their best performing metapath. Since a metapath's performance is limited by its least informative metaedge, the best performing metapath for a metaedge provides a lower bound on the pharmacologic utility of a given domain of information. B) Barplot of the model coefficients. Features were standardized prior to model fitting to make the coefficients comparable .
Overall, 709 of the 1,206 metapaths exhibited a statistically significant Δ AUROC at a false discovery rate cutoff of 5%. These 709 metapaths included all 24 metaedges, suggesting that each type of relationship we integrated provided at least some therapeutic utility. However, not all metaedges were equally present in significant metapaths: 259 significant metapaths included a Compound–binds–Gene metaedge, whereas only 4 included a Gene–participates–Cellular Component metaedge. Table 3 lists the predictiveness of several metapaths of interest. Refer to the Discussion for our interpretation of these findings.
Table 3. The predictiveness of select metapaths
A small selection of the 1,206 metapaths is provided. Len. refers to number of metaedges composing the metapath. Δ AUROC and −log10(p) assess the performance of a metapath's DWPC in discriminating treatments from non-treatments (in the all-features stage as described in Methods). p assesses whether permutation affected AUROC. For reference, p = 0.05 corresponds to −log10(p) = 1.30. Note that several metapaths shown here provided little evidence that Δ AUROC ≠ 0 underscoring their poor ability to predict whether a compound treated a disease. Coef. reports a metapath's logistic regression coefficient as seen in Figure 2B. Metapaths removed in feature selection have missing coefficients whereas metapaths given zero-weight by the elastic net have coef. = 0.0.
Predictions of drug efficacy
We implemented a machine learning approach to translate the network connectivity between a compound and a disease into a probability of treatment. The approach relies on the 755 known treatments as positives and 29,044 non-treatments as negatives to train a logistic regression model. The features consisted of a prior probability of treatment, node degrees for 14 metaedges, and DWPCs for 123 metapaths that were well suited for modeling. A cross-validated elastic net was used to minimize overfitting, yielding a model with 31 features (Figure 2B). The DWPC features with negative coefficients appear to be included as node-degree-capturing covariates, i.e. they reflect the general connectivity of the compound and disease rather than specific paths between them. However, the 11 DWPC features with non-negligible positive coefficients represent the most salient types of connectivity for systematically modeling drug efficacy. See the metapaths with positive coefficients in Table 3 for unabbreviated names. As an example, the CcSEcCtD feature assesses whether the compound causes the same side effects as compounds that treat the disease. Alternatively, the CbGeAlD feature assesses whether the compound binds to genes that are expressed in the anatomies affected by the disease.
We applied this model to predict the probability of treatment between each of 1,538 connected compounds and each of 136 connected diseases, resulting in predictions for 209,168 compound–disease pairs , available at http://het.io/repurpose/. The 755 known disease-modifying indications were highly ranked (AUROC = 97.4%, Figure 3). The predictions also successfully prioritized two external validation sets: novel indications from DrugCentral (AUROC = 85.5%) and novel indications in clinical trial (AUROC = 70.0%). Together, these findings indicate that Project Rephetio has the ability to recognize efficacious compound–disease pairs.
Figure 3. Predictions performance on four indication sets
We assess how well our predictions prioritize four sets of indications. A) The y-axis labels denote the number of indications (+) and non-indications (−) composing each set. Violin plots with quartile lines show the distribution of indications when compound–disease pairs are ordered by their prediction. In all four cases, the actual indications were ranked highly by our predictions. B) ROC Curves with AUROCs in the legend. C) Precision–Recall Curves with AUPRCs in the legend.
Predictions were scaled to the overall prevalence of treatments (0.36%). Hence a compound–disease pair that received a prediction of 1% represents a 2-fold enrichment over the null probability. Of the 3,980 predictions with a probability exceeding 1%, 586 corresponded to known disease-modifying indications, leaving 3,394 repurposing candidates. For a given compound or disease, we provide the percentile rank of each prediction. Therefore, users can assess whether a given prediction is a top prediction for the compound or disease. In addition, our table-based prediction browser links to a custom guide for each prediction, which displays in the Neo4j Hetionet Browser. Each guide includes a query to display the top paths supporting the prediction and lists clinical trials investigating the indication.
Nicotine dependence case study
There are currently two FDA-approved medications for smoking cessation (varenicline and bupropion) that are not nicotine replacement therapies. PharmacotherapyDB v1.0 lists varenicline as a disease-modifying indication and nicotine itself as a symptomatic indication for nicotine dependence, but is missing bupropion. Bupropion was first approved for depression in 1985. Owing to the serendipitous observation that it decreased smoking in depressed patients taking this drug, Bupropion was approved for smoking cessation in 1997 . Therefore we looked whether Project Rephetio could have predicted this repurposing. Bupropion was the 9th best prediction for nicotine dependence (99.5th percentile) with a probability 2.50-fold greater than the null. Figure 4 shows the top paths supporting the repurposing of bupropion.
Figure 4. Evidence supporting the repurposing of bupropion for smoking cessation
This figure shows the 10 most supportive paths (out of 365 total) for treating nicotine dependence with bupropion, as available in this prediction's Neo4j Browser guide. Our method detected that bupropion targets the CHRNA3 gene, which is also targeted by the known-treatment varenicline . Furthermore, CHRNA3 is associated with nicotine dependence  and participates in several pathways that contain other nicotinic-acetylcholine-receptor (nAChRs) genes associated with nicotine dependence. Finally, bupropion causes terminal insomnia  as does varenicline , which could indicate an underlying common mechanism of action.
Atop the nicotine dependence predictions were nicotine (10.97-fold over null), cytisine (10.58-fold), and galantamine (9.50-fold). Cytisine is widely used in Eastern Europe for smoking cessation due to its availability at a fraction of the cost of other pharmaceutical options . In the last half decade, large scale clinical trials have confirmed cytisine's efficacy [46, 47]. Galantamine, an approved Alzheimer's treatment, is currently in Phase 2 trial for smoking cessation and is showing promising results . In summary, nicotine dependence illustrates Project Rephetio's ability to predict efficacious treatments and prioritize historic and contemporary repurposing opportunities.
Epilepsy case study
Several factors make epilepsy an interesting disease for evaluating repurposing predictions . Antiepileptic drugs work by increasing the seizure threshold — the amount of electric stimulation that is required to induce seizure. The effect of a drug on the seizure threshold can be cheaply and reliably tested in rodent models. As a result, the viability of most approved drugs in treating epilepsy is known.
We focused our evaluation on the top 100 scoring compounds — referred to as the epilepsy predictions in this section — after discarding a single combination drug. We classified each compound as anti-ictogenic (seizure suppressing), unknown (no established effect on the seizure threshold), or ictogenic (seizure generating) according to medical literature . Of the epilepsy predictions, 77 were anti-ictogenic, 8 were unknown, and 15 were ictogenic (Figure 5). Notably, the predictions contained 23 of the 25 disease-modifying antiepileptics in PharamcotherapyDB v1.0.
Figure 5. Top 100 epilepsy predictions colored by their effect on seizures
The predicted probability of treatment versus prediction rank is plotted for the top 100 epilepsy predictions. Note that all compounds shown received probabilities far exceeding the null probability of treatment. Furthermore, the highest predictions are almost exclusively anti-ictogenic. Further down the prediction list the prevalence of drugs with an ictogenic (contraindications) or unknown (novel repurposing candidates) effect on epilepsy increases.
Many of the 77 anti-ictogenic compounds were not first-line antiepileptic drugs. Instead, they were used as ancillary drugs in the treatment of status epilepticus. For example, we predicted four halogenated ethers, two of which (isoflurane and desflurane) are used clinically to treat life-threatening seizures that persist despite treatment . As inhaled anesthetics, these compounds are not appropriate as daily epilepsy medications, but are feasible for refractory status epilepticus where patients are intubated.
Given this high precision (77%), the 8 compounds of unknown effect are promising repurposing candidates. For example, acamprosate — whose top prediction was epilepsy — is a taurine analog that promotes alcohol abstinence. Support for this repurposing arose from acamprosate's positive modulation of the GABAᴬ receptor and inhibition of the glutamate receptor. If effective against epilepsy, acamprosate could serve a dual benefit for recovering alcoholics who experience seizures from alcohol withdrawal.
Also notable are the 15 ictogenic compounds in our top 100 predictions. As an example, we predicted five tricyclic antidepressants primarily based on their binding to the GABAᴬ receptor. However, these compounds are GABAᴬ antagonists, rather than agonists, likely resulting in their ictogenic properties.
As isoflurane, desflurane, and acamprosate demonstrate, Project Rephetio is capable of predicting repurposing candidates that fulfil a therapeutic niche. In addition, a portion of Rephetio's predictions are likely contraindications. However, in the case of epilepsy, where the effect of most approved drugs is known, our approach was still able to overwhelmingly prioritize ictogenic compounds.
We created Hetionet v1.0 by integrating 29 resources into a single data structure — the hetnet. Consisting of 11 types of nodes and 24 types of relationships, Hetionet v1.0 brings more types of information together than previous leading-studies in biological data integration . Moreover, we strove to create a reusable, extensible, and property-rich network. While all of the resources we include are publicly available, their integration was a time-intensive undertaking. Hetionet allows researchers to begin answering integrative questions without having to first spend months processing data.
Our public Neo4j instance allows users to immediately interact with Hetionet. Through the Cypher language, users can perform highly specialized graph queries with only a few lines of code. Queries can be executed in the web browser or programmatically from a language with a Neo4j driver. For users that are unfamiliar with Cypher, we include several example queries in a Browser guide. In contrast to traditional REST APIs, our public Neo4j instance provides users with maximal flexibility to construct custom queries by exposing the underlying database.
As data has grown more plentiful and diverse, so has the applicability of hetnets. Unfortunately, network science has been naturally fragmented by discipline resulting in relatively slow progress in integrating heterogeneous data. A 2014 analysis identified 78 studies using multilayer networks — a superset of hetnets with the potential for a time dimension. However, the studies relied on 26 different terms, 9 of which had multiple definitions [52, 53]. Nonetheless, core infrastructure and algorithms for hetnets are emerging. One goal of our project has been to unite hetnet research across disciplines. We approached this goal by making Project Rephetio entirely available online and inviting community feedback throughout the process .
Integrating every resource into a single interconnected data structure allowed us to assess systematic mechanisms of drug efficacy. Using the max performing metapath to assess the pharmacological utility of a metaedge (Figure 2A), we can divide our relationships into tiers of informativeness. The top tier consists of the types of information traditionally considered by pharmacology: Compound–treats–Disease, Pharmacologic Class–includes–Compound, Compound–resembles–Compound, Disease–resembles–Disease, and Compound–binds–Gene. The upper-middle tier consists of types of information that have been the focus of substantial medical study, but have only recently started to play a bigger role in drug development, namely the metaedges Disease–associates–Gene, Compound–causes–Side Effect, Disease–presents–Symptom, Disease–localizes–Anatomy, and Gene–interacts–Gene.
The lower-middle tier contains the transcriptomics metaedges such as Compound–downregulates–Gene, Anatomy–expresses–Gene, Gene→regulates→Gene, and Disease–downregulates–Gene. Much excitement surrounds these resources due to their high throughput and genome-wide scope, which offers a route to drug discovery that is less biased by existing knowledge. However, our findings suggest that these resources are only moderately informative of drug efficacy. Other lower-middle tier metaedges were the product of time-intensive biological experimentation, such as Gene–participates–Pathway, Gene–participates–Molecular Function, and Gene–participates–Biological Process. Unlike the top tier resources, this knowledge has historically been pursued for basic science rather than primarily medical applications. The weak yet appreciable performance of the Gene–covaries–Gene suggests the synergy between the fields of evolutionary genomics and disease biology. The lower tier included the Gene–participates–Cellular Component metaedge, which may reflect that the relevance of cellular location to pharmacology is highly case dependent and not amenable to systematic profiling.
The performance of specific metapaths (Table 3) provides further insight. For example, significant emphasis has been put on the use of transcriptional data for drug repurposing . One common approach has been to identify compounds with opposing transcriptional signatures to a disease [55, 18]. However, several systematic studies report underwhelming performance of this approach [25, 24, 26] — a finding supported by the low performance of the CuGdD and CdGuD metapaths in Project Rephetio. Nonetheless, other transcription-based methods showed some promise. Compounds with similar transcriptional signatures were prone to treating the same disease (CuGuCtD and CdGdCtD metapaths), while compounds with opposing transcriptional signatures were slightly averse to treating the same disease (CuGdCtD and CdGuCtD metapaths). In contrast, diseases with similar transcriptional profiles were not prone to treatment by the same compound (CtDdGuD and CtDuGdD).
By comparably assessing the informativeness of different metaedges and metapaths, Project Rephetio aims to guide future research towards promising data types and analyses. Encouragingly, most data types were at least weakly informative and hence suitable for further study. Ideally, different data types would provide orthogonal information. However, our model for whether a compound treats a disease focused on 11 metapaths — a small portion of the hundreds of metapaths available. While parsimony aids interpretation, our model did not draw on the weakly-predictive high-throughput data types — which are intriguing for their novelty, scalability, and cost-effectiveness — as much as we had hypothesized.
Instead our model selected types of information traditionally considered in pharmacology. However unlike a pharmacologist whose area of expertise may be limited to a few drug classes, our model was able to predict probabilities of treatment for all 209,168 compound–disease pairs. Furthermore, our model systematically learned the importance of each type of network connectivity. For any compound–disease pair, we now can immediately provide the top network paths supporting its therapeutic efficacy. A traditional pharmacologist may be able to produce a similar explanation, but likely not until spending substantial time researching the compound's pharmacology, the disease's pathophysiology, and the molecular relationships in between. Accordingly, we hope certain predictions will spur further research, such as trials to investigate the off-label use of acamprosate for epilepsy.
As demonstrated by the 15 ictogenic compounds in our top 100 epilepsy predictions, Project Rephetio's predictions can include contraindications in addition to indications. Since many of Hetionet v1.0's relationship types are general (e.g. the Compound–binds–Gene relationship type conflates antagonist with agonist effects), we expect some high scoring predictions to exacerbate rather than treat the disease. However, the predictions made by Hetionet v1.0 represent such substantial relative enrichment over the null that uncovering the correct directionality is a logical next step and worth undertaking. Going forward, advances in automated mining of the scientific literature could enable extraction of precise relationship types at omics scale .
Future research should focus on gleaning orthogonal information from data types that are so expansive that computational methods are the only option. Our CuGuCtD feature — measuring whether a compound upregulates the same genes as compounds which treat the disease — is a good example. This metapath was informative by itself (Δ AUROC = 4.4%) but was not selected by the model, despite its orthogonal origin (gene expression) to selected metapaths. Using a more extensive catalog of treatments as the gold standard would be one possible approach to increase the power of feature selection.
Integrating more types of information into Hetionet should also be a future priority. The "network effect" phenomenon suggests that the addition of each new piece of information will enhance the value of Hetionet's existing information. We envision a future where all biological knowledge is encoded into a single hetnet. Hetionet v1.0 was an early attempt, and we hope the strong performance of Project Rephetio in repurposing drugs foreshadows the many applications that will thrive from encoding biology in hetnets.
Hetionet was built entirely from publicly available resources with the goal of integrating a broad diversity of information types of medical relevance, ranging in scale from molecular to organismal. Practical considerations such as data availability, licensing, reusability, documentation, throughput, and standardization informed our choice of resources. We abided by a simple litmus test for determining how to encode information in a hetnet: nodes represent nouns, relationships represent verbs [57, 58].
Our method for relationship prediction creates a strong incentive to avoid redundancy, which increases the computational burden without improving performance. In a previous study to predict disease–gene associations using a hetnet of pathophysiology , we found that different types of gene sets contributed highly redundant information. Therefore, in Hetionet v1.0 we reduced the number of gene set node types from 14 to 3 by omitting several gene set collections and aggregating all pathway nodes.
Nodes encode entities. We extracted nodes from standard terminologies, which provide curated vocabularies to enable data integration and prevent concept duplication. The ease of mapping external vocabularies, adoption, and comprehensiveness were primary selection criteria. Hetionet v1.0 includes nodes from 5 ontologies — which provide hierarchy of entities for a specific domain — selected for their conformity to current best practices .
We selected 137 terms from the Disease Ontology [60, 61] (which we refer to as DO Slim [62, 63]) as our disease set. Our goal was to identify complex diseases that are distinct and specific enough to be clinically relevant yet general enough to be well annotated. To this end, we included diseases that have been studied by GWAS and cancer types from
Approved small molecule compounds with documented chemical structures were extracted from DrugBank version 4.2 [67, 68, 69]. Unapproved compounds were excluded because our focus was repurposing. In addition, unapproved compounds tend to be less studied than approved compounds making them less attractive for our approach where robust network connectivity is critical. Finally, restricting to small molecules with known documented structures enabled us to map between compound vocabularies (see Mappings).
Protein-coding human genes were extracted from Entrez Gene [75, 76, 77]. Anatomical structures, which we refer to as anatomies, were extracted from Uberon . We selected a subset of 402 Uberon terms by excluding terms known not to exist in humans and terms that were overly broad or arcane [79, 80].
Pathways were extracted by combining human pathways from WikiPathways [81, 82], Reactome , and the Pathway Interaction Database . The latter two resources were retrieved from Pathway Commons , which compiles pathways from several providers. Duplicate pathways and pathways without multiple participating genes were removed [86, 87]. Biological processes, cellular components, and molecular functions were extracted from the Gene Ontology . Only terms with 2–1000 annotated genes were included.
Before adding relationships, all identifiers needed to be converted into the vocabularies matching that of our nodes. Oftentimes, our node vocabularies included external mappings. For example, the Disease Ontology includes mappings to MeSH, UMLS, and the ICD, several of which we submitted during the course of this study . In a few cases, the only option was to map using gene symbols, a disfavored method given that it can lead to ambiguities.
Compound–binds–Gene edges were aggregated from BindingDB [101, 102], DrugBank [103, 67], and DrugCentral. Only binding relationships to single proteins with affinities of at least 1 μM (as determined by Kd, Ki, or IC50) were selected from the October 2015 release of BindingDB [104, 105]. Target, carrier, transporter, and enzyme interactions with single proteins (i.e. excluding protein groups) were extracted from DrugBank 4.2 [106, 69]. In addition, all mapping DrugCentral target relationships were included .
Compound–treats–Disease (disease-modifying indications) and Compound–palliates–Disease (symptomatic indications) edges are from PharmacotherapyDB as described in Intermediate resources. Compound–causes–Side Effect edges were obtained from SIDER 4.1 [70, 71, 72], which uses natural language processing to identify side effects in drug labels. Compound–resembles–Compound relationships [107, 69, 108] represent chemical similarity and correspond to a Dice coefficient ≥ 0.5  between extended connectivity fingerprints [110, 111]. Compound–downregulates–Gene and Compound–upregulates–Gene relationships were computed from LINCS L1000 as described in Intermediate resources.
Disease–associates–Gene edges were extracted from the GWAS Catalog , DISEASES [113, 114], DisGeNET [115, 116], and DOAF [117, 118]. The GWAS Catalog compiles disease–SNP associations from published GWAS . We aggregated overlapping loci associated with each disease and identified the mode reported gene for each high confidence locus [120, 121]. DISEASES integrates evidence of association from text mining, curated catalogs, and experimental data . Associations from DISEASES with integrated scores ≥ 2 were included after removing the contribution of DistiLD. DisGeNET integrates evidence from over 10 sources and reports a single score for each association . Associations with scores ≥ 0.06 were included. DOAF mines Entrez Gene GeneRIFs (textual annotations of gene function) for disease mentions . Associations with 3 or more supporting GeneRIFs were included. Disease–downregulates–Gene and Disease–upregulates–Gene relationships [125, 126] were computed using STARGEO as described in Intermediate resources.
Disease–localizes–Anatomy, Disease–presents–Symptom, and Disease–resembles–Disease edges were calculated from MEDLINE co-occurrence [65, 127]. MEDLINE is a subset of 21 million PubMed articles for which designated human curators have assigned topics. When retrieving articles for a given topic (MeSH term), we activated two non-default search options as specified below:
Gene–covaries–Gene edges represent evolutionary rate covariation ≥ 0.75 [130, 131, 132]. Gene–interacts–Gene edges [133, 134] represent when two genes produce physically-interacting proteins. We compiled these interactions from the Human Interactome Database [135, 136, 137, 138], the Incomplete Interactome , and our previous study . Gene–participates–Biological Process, Gene–participates–Cellular Component, and Gene–participates–Molecular Function edges are from Gene Ontology annotations . As described in Intermediate resources, annotations were propagated [141, 142].
Transcriptional signatures of disease using STARGEO
STARGEO is a nascent platform for annotating and meta-analyzing differential gene expression experiments. The STAR acronym stands for Search-Tag-Analyze Resources, while GEO refers to the Gene Expression Omnibus [143, 144]. STARGEO is a layer on top of GEO that crowdsources sample annotation and automates meta-analysis.
Using STARGEO, we computed differentially expressed genes between healthy and diseased samples for 49 diseases [125, 126]. First, we and others created case/control tags for 66 diseases. After combing through GEO series and tagging samples, 49 diseases had sufficient data for case-control meta-analysis: multiple series with at least 3 cases and 3 controls. For each disease, we performed a random effects meta-analysis on each gene to combine log2 fold-change across series. These analyses incorporated 27,019 unique samples from 460 series on 107 platforms.
Differentially expressed genes (false discovery rate ≤ 0.05) were identified for each disease. The median number of upregulated genes per disease was 351 and the median number of downregulated genes was 340. Endogenous depression was the only of the 49 diseases without any significantly dysregulated genes.
Transcriptional signatures of perturbation from LINCS L1000
LINCS L1000 profiled the transcriptional response to small molecule and genetic interference perturbations. To increase throughput, expression was only measured for 978 genes, which were selected for their ability to impute expression of the remaining genes. A single perturbation was often assayed under a variety of conditions including cell types, dosages, timepoints, and concentrations. Each condition generates a single signature of dysregulation z-scores. We further processed these signatures to fit into our approach [145, 146].
First we computed consensus signatures — which meta-analyze multiple signatures to condense them into one — for DrugBank small molecules, Entrez genes, and all L1000 perturbations [147, 148]. First, we discarded non-gold (non-replicating or indistinct) signatures. Then we meta-analyzed z-scores using Stouffer's method. Each signature was weighted by its average Spearman's correlation to other signatures, with a 0.05 minimum, to de-emphasize discordant signatures. Our signatures include the 978 measured genes and the 6,489 imputed genes from the "best inferred gene subset". To identify significantly dysregulated genes, we selected genes using a Bonferroni cutoff of p = 0.05 and limited the number of imputed genes to 1,000.
The consensus signatures for genetic perturbations allowed us to assess various characteristics of the L1000 dataset. First, we looked at whether genetic interference dysregulated its target gene in the expected direction . Looking at measured z-scores for target genes, we found that the knockdown perturbations were highly reliable, while the overexpression perturbations were only moderately reliable with 36% of overexpression perturbations downregulating their target. However, imputed z-scores for target genes barely exceeded chance at responding in the expected direction to interference. Hence, we concluded that the imputation quality of LINCS L1000 is poor. However, when restricting to significantly dyseregulated targets, 22 out of 29 imputed genes responded in the expected direction. This provides some evidence that the directional fidelity of imputation is higher for significantly dysregulated genes. Finally, we found that the transcriptional signatures of knocking down and overexpressing the same gene were positively correlated 65% of the time, suggesting the presence of a general stress response .
Based on these findings, we performed additional filtering of signifcantly dysregulated genes when building Hetionet v1.0. Compound–down/up-regulates–Gene relationships were restricted to the 125 most significant per compound-direction-status combination (status refers to measured versus imputed). For genetic interference perturbations, we restricted to the 50 most significant genes per gene-direction-status combination and merged the remaining edges into a single Gene→regulates→Gene relationship type containing both knockdown and overexpression perturbations.
This resource was motivated by the need for a gold standard of medical indications to train and evaluate our approach. Initially, we identified four existing indication catalogs : MEDI-HPS which mined indications from RxNorm, SIDER 2, MedlinePlus, and Wikipedia ; LabeledIn which extracted indications from drug labels via human curation [156, 157, 158]; EHRLink which identified medication–problem pairs that clinicians linked together in electronic health records [159, 160]; and indications from PREDICT, which were compiled from UMLS relationships, drugs.com, and drug labels . After mapping to DO Slim and DrugBank Slim, the four resources contained 1,388 distinct indications.
However, we noticed that many indications were palliative and hence problematic as a gold standard of pharmacotherapy for our in silico approach. Therefore, we recruited two practicing physicians to curate the 1,388 preliminary indications . After a pilot on 50 indications, we defined three classifications: disease modifying meaning a drug that therapeutically changes the underlying or downstream biology of the disease; symptomatic meaning a drug that treats a significant symptom of the disease; and non-indication meaning a drug that neither therapeutically changes the underlying or downstream biology nor treats a significant symptom of the disease. Both curators independently classified all 1,388 indications.
The two curators disagreed on 444 calls (Cohen's κ = 49.9%). We then recruited a third practicing physician, who reviewed all 1,388 calls and created a detailed explanation of his methodology . We proceeded with the third curator's calls as the consensus curation. The first two curators did have reservations with classifying steroids as disease modifying for autoimmune diseases. We ultimately considered that these indications met our definition of disease modifying, which is based on a pathophysiological rather than clinical standard. Accordingly, therapies we consider disease modifying may not be used to alter long-term disease course in the modern clinic due to a poor risk–benefit ratio.
User-friendly Gene Ontology annotations
We created a browser (http://git.dhimmel.com/gene-ontology/) to provide straightforward access to Gene Ontology annotations [142, 141]. Our service provides annotations between Gene Ontology terms and Entrez Genes. The user chooses propagated/direct annotation and all/experimental evidence. Annotations are currently available for 37 species and downloadable as user-friendly TSV files.
Data copyright and licensing
We committed to openly releasing our data and analyses from the origin of the project . Our goals were to contribute to the advancement of science [163, 164], maximize our impact , and enable reproducibility [166, 167, 168]. These objectives required publicly distributing and openly licensing Hetionet and Project Rephetio data and analyses [169, 170].
Of the 29 resources we integrated, only 12 had licenses that met the Open Definition with respect to knowledge. 9 did not have a license, which equates to all rights reserved and by default forbids reuse . Several resources had incompatible licenses caused primarily by non-commercial and share-alike stipulations. One resource included terms which explicitly forbid redistribution. In addition, it was often unclear who owned the data . Therefore, we sought input from legal experts and chronicled our progress [174, 175, 176, 177].
Ultimately, we did not find an ideal solution. We had to choose between absolute compliance and Hetionet: strictly adhering to copyright and licensing arrangements would have decimated the network. Hence we choose a path forward which balanced legal, normative, ethical, and scientific considerations. If a resource was in the public domain, for example works of the US Government, we licensed any derivatives as CC0 1.0. For resources licensed to allow use, redistribution, and modification, we transmitted their licenses as properties on the specific nodes and relationships in our hetnet. For all other resources — for example, resources without licenses or with licenses that forbid redistribution — we sent permission requests to their creators. The median time till first response to our permission requests was 16 days, with only 2 resources affirmatively granting us permission. We did not receive any responses asking us to remove a resource. However, we did voluntarily remove MSigDB , since its license was highly problematic .
From Hetionet, we derived five permuted hetnets . The permutations preserve node degree but eliminate edge specificity by employing an algorithm called XSwap to randomly swap edges . Permuted networks are useful for computing the baseline performance of meaningless edges while preserving node degree .
Graph database adoption in bioinformatics has thus far been limited . We used the Neo4j graph database for storing and operating on Hetionet and noticed major benefits from tapping into this large open source ecosystem . Persistent storage with immediate access and the Cypher query language — a sort of SQL for hetnets — were two of the biggest benefits. To facilitate our migration to Neo4j, we updated
Machine learning approach
We made several refinements to metapath-based hetnet edge prediction compared to previous studies [22, 23]. First, we transformed DWPCs by mean scaling and then taking the inverse hyperbolic sine  to make them more amenable to modeling . Second, we bifurcated the workflow into an all-features stage and an all-observations stage . The all-features stage assesses feature performance and does not require computing features for all negatives. Here we selected a random subset of 3,020 (4 × 755) negatives. Little error was introduced by this optimization, since the predominant limitation to performance assessment was the small number of positives (755) rather than negatives. Based on the all-features performance assessment , we selected 142 DWPCs to compute on all observations (all 209,168 compound–disease pairs). The feature selection was designed to remove uninformative features (according to permutation) and guard against edge-dropout contamination . Third, we included 14 degree features, which assess the degree of a specific metaedge for either the source compound or target disease.
Prior probability of treatment
The 755 treatments in Hetionet v1.0 are not evenly distributed between all compounds and diseases. For example, methotrexate treats 19 diseases and hypertension is treated by 68 compounds. We estimated a prior probability of treatment — based only on the treatment degree of the source compound and target disease — on 744,975 permutations of the bipartite treatment network . Methotrexate received a 79.6% prior probability of treating hypertension, whereas a compound and disease that both had only one treatment received a prior of 0.12%.
Across the 209,168 compound–disease pairs, the prior predicted the known treatments with AUROC = 97.9%. The strength of this association threatened to dominate our predictions. However, not modeling the prior can lead to omitted-variable bias and confounded proxy variables. To address the issue, we included the logit-transformed prior, without any regularization, as a term in the model. This restricted model fitting to the 29,799 observations with a nonzero prior — corresponding to the 387 compounds and 77 diseases with at least one treatment. To enable predictions for all 209,168 observations, we set the prior for each compound–disease pair to the overall prevalence of positives (0.36%).
This method succeeded at accommodating the treatment degrees. The prior probabilities performed poorly on the validation sets with AUROC = 54.1% on DrugCentral indications and AUROC = 62.5% on clinical trials. This performance dropoff compared to training shows the danger of encoding treatment degree into predictions. The benefits of our solution are highlighted by the superior validation performance of our predictions compared to the prior (Figure 3).
Realtime open science & Thinklab
We conducted our study using Thinklab — a platform for realtime open collaborative science — on which this study was the first project. We began the study by publicly proposing the idea and inviting discussion . We continued by chronicling our progress via discussions. We used Thinklab as the frontend to coordinate and report our analyses and GitHub as the backend to host our code, data, and notebooks. On top of our Thinklab team consisting of core contributors, we welcomed community contribution and review. In areas where our expertise was lacking or advice would be helpful, we sought input from domain experts and encouraged them to respond on Thinklab where their comments would be CC BY licensed and their contribution rated and rewarded.
In total, 36 non-team members commented across 80 discussions, which generated 488 comments and 161 notes (Figure 6). The Thinklab content for this project totaled 111,425 words or 698,830 characters . Using an estimated 7,000 words per academic publication as a benchmark, Project Rephetio generated written content comparable in volume to 15.9 publications prior to its completion. We noticed several other benefits from using Thinklab including forging a community of contributors ; receiving feedback during the early stages when feedback is the most actionable ; disseminating our research without delay [200, 201]; opening avenues for external input ; facilitating problem-oriented teaching [203, 204]; and improving our documentation by maintaining a publication-grade digital lab notebook .
Figure 6. The growth the Project Rephetio corpus on Thinklab over time
This figure shows Project Rephetio contributions by user over time. Each band represented the cumulative contribution of a Thinklab user to discussions in the Rephetio project . Users are ordered by date of first contribution. Users who contributed over 4,000 characters are named. The square root transformation of characters written per user accentuates the activity of new contributors, thereby emphasizing collaboration and diverse input.
We are immensely grateful to our Thinklab contributors who joined us in our experiment of radically open science. The following non-team members provided contributions that received 5 or more Thinklab points: Lars Juhl Jensen, Frederic Bastian, Alexander Pico, Casey Greene, Craig Knox, Benjamin Good, Chris Mungall, Katie Fortney, Venkat Malladi, MacKenzie Smith, Caty Chung, Mike Gilson, Tudor Oprea, Allison McCoy, Alexey Strokach, Ritu Khare, Marina Sirota, Greg Way, Raghavendran Partha, Jesse Spaulding, Alessandro Didonna, Alex Pankov, Janet Piñero, Oleg Ursu, Tong Shu Li. We would also like to thank Neo Technology, whose staff provided excellent technical support.
Queries such as this result in a timeout on Hetionet's Neo4j browser:
I think you have the following query you have in mind:
This query looks for all paths of length four between any symptom node and any anatomy node. However, Figure 1C shows the number of metapaths (types of paths), not the overall number of paths. I modified your query to return distinct metapaths (and changed Anatomy to Compound and length 4 to 2 to make things more computationally tractable):
You can read more about Figure 1C here, When we calculated the number of metapaths (notebook), we didn't use Neo4j at all. Instead we used our
So were most of the metapath (type) counts done using Python instead of Cypher? I wondered if one of the reasons for migrating the network to Neo4j was for improving feature extraction, e.g. metapath counts.
All operations on the metagraph are done in Python, as Neo4j does not have any way to interact with the metagraph. Figure 1, Table 1, and Table 2 are created using the
The adoption of Neo4j was primarily motivated by the need for immediate network access and a versatile graph query language. Regarding feature extraction, both
What are the the features in the Degree box above and DWPC box below? How is the logistic regression coefficient calculated?
Figure 2B shows the logistic regression coefficients as described here.
We have three types of features: the prior (not shown in Figure 2), degree features (showin in Figure 2B), and DWPCs (shown in Figure 2A–B). Degree features
Why are these meta paths different to those in Fig 1?
All of these metapaths can be generated by walking the metagraph in Figure 1A.
Typo, it should be: Why are these meta paths different to those in Fig 2B?
Figure 2B shows the features selected by the logistic regression model. This table shows metapaths that we think are noteworthy. As we discuss below, I generally tried to include all metapaths with "non-negligible positive coefficients" in this table, since those features play such an important role in our predictions.
Since the logistic regression was minimalist and conservative, it selected only a small subset of the predictive features. Additionally, many predictive features are highly correlated and will therefore be filtered out by the elastic net. Nonetheless these features are interesting because their performance (Δ AUROC) indicates its utility for predicting drug efficacy. See our comments on many of these metapaths in the discussion.
How did you separate the dataset into train and test data? Since it has imbalanced labels, the way of data selection seems critical to the performance.
We didn't use the training / testing language in the report, since our approach is slightly more complicated. However, in our case "testing performance" would refer to performance on any indication set whose positives or negatives do not include any treats or palliates relationships in Hetionet v1.0. Of the indication sets we compiled, DrugCentral and Clinical Trial meet this criterion. Hence, we call them "external validation sets".
In Figure 3A, look at the number of positives and negatives in the y-axis labels. Disease Modifying, the "training set", includes 755 positives and 208,413 negatives, which together make up all 209,168 possible compound–disease pairs. Symptomatic, whose positives are palliates relationships in Hetionet v1.0, splits the 208,413 observations into 390 positives and 208,023 negatives. Hence, the two "testing sets" both have 208,023 observations. In other words, their positives were negatives in the "training set".
Now a few points that are worth considering.
@hanockkwak, does that answer your question?
Thank you for the detailed explanation.
We did not withhold compound–disease pairs from the "Disease Modifying" indication set for testing. I explain this decision here . Hence, the "Disease Modifying" AUROC is calculated on observations that we used to train. While I'm confident that our logistic regression model doesn't overfit due the cross-validated regularization, you should still regard the "Disease Modifying" AUROC as a training (not testing) AUROC.
For testing AUROCs, please look to the two external validation sets:
Only see 30 features in Fig 2B
The prior is a feature but is not shown in Figure 2B. It was omitted since it's
Only see 10 in Table 3 with positive coefficients
Great 👀. The missing feature is CrCbGaD (Compound–resembles–Compound–binds–Gene–associates–Disease). I should either consider the coefficient for this feature negligible or include the feature in Table 3 (what I'm leaning towards). Note there's an online version of Table 3 with all metapaths.
What is the 4th dataset, Symptomatic? Is this in any way related to the 'palliates' relationship?
See Indication setshttps://think-lab.github.io/doc/7/review#section-87
So yes they are the palliates relationships.
In this figure, CHRNA3 should participate in not cause "Nicotine Activity on Chromaffin Cells."
Should this be nAChR rather than nAChRs? Or nAChRs but after genes?
yes I think the usual nomenclature is AChR or nAChR.
the "s" would be to make is plural which I don't think is needed
Unfortunately unicode doesn't currently contain a subscript capital letter A. However, "GABAᴀ" may be better than "GABAᴬ" as a unicode representation for the GABA alpha receptor. The small capital A more closely imitates a subscript A.
How long did the data integration take? Was raw data to hetnet conversion more laborious than migrating hetnets to Hetionet?
Here's the notebook that creates Hetionet using the
It took me about a year to construct the network. However, you could now repeat the process in less time. I believe @tongli has repeated much of the process, with some tweaks. @tongli do you want to comment on how long things take.
Importing the JSON/hetio format for storing the hetnet takes about a day on a decent system.
Tong Shu Li
The actual runtime of the individual notebooks (especially the data integration ones) has never been the problem for me. Rather it is understanding and modifying the notebooks which is time intensive and difficult.
I have had success running the data integration portion of the full sized Hetionet on AWS, but am currently working on a cross-validation evaluation scheme which is currently using a substantially reduced network (consisting only of gene, disease, compound nodes and their relations).
My personal version of the data integration code can be found here.
Add "which is supported by one animal model ." See this comment for more detail. Credit to @pouyakhankhanian for this suggesting and finding the study.
Could you specify the steps and subset of data taken for each of your source? This is my understanding so far of the process:
a) Could you confirm that the above information is correct for items 0,1,2,6 and 8?
For reference, the best way to determine the exact steps that went into creating any node or relationship is to look at the
Confirming that @gaya_n's understanding of how nodes were created is correct for points 0, 1, 2, 6, 8.
Compound side effects from SIDER 4.1 were mapped to DrugBank compounds. The UMLS concept identifiers corresponding to MedDRA side effects, as provided by SIDER, were used to uniquely identify side effects. If a UMLS side effect was not associated with any DrugBank compounds, it was removed. 5,734 side effect nodes remained. 33 side effects nodes are disconnected in Hetionet v1.0, since Hetionet v1.0 does not include every DrugBank compound. Fore more information, see the SIDER processing source notebook.
4) Pharmacologic class (DrugCentral) - which ones?
See this comment, cell 15 of
All protein-coding homo sapiens genes from EntrezGene. See discussion.
Yes, see this comment and the source notebook. Only WikiPathways for homo sapiens were included.
Sometimes relationships were created for all terms. Sometimes relationships were only created for nodes in Hetionet v1.0. Sometimes nodes in Hetionet v1.0 (e.g. pharmacologic classes) are the only the nodes with relationships in Hetionet v1.0. There is no categorical answer here, you'll have to look at the source code.
Only see two mentioned; GO and Disease Ontology
The five ontologies are:
What were the criteria for meta edges selection? Were they a certain group of relationships you sought after or did you try to obtain as many relationships as possible?
We were most interested in relationships that were plausibly informative for modeling drug efficacy. We also focused on integrating relationships from high-throughput/systematic methods (see for example LINCS, STARGEO, Bgee, evolutionary rate covariation, and the Human Interactome Database) that we hoped would help make novel predictions. In addition,
Consider updating GWAS Catalog citation to
Is the data used to evaluate this work (209,168 compound–disease pairs and their metapaths), open freely for experimentation?
Yes, it's available under a CC0 public domain dedication.
If you have questions on data or other content in
How long did the (logistic regression) training take given that you have strong predictive features?
Fitting the logistic regression model using
Overall, feature extraction was much more computationally insensitive than model fitting. Getting feature extraction to complete in a reasonable time took query optimizations, complexity benchmarking, and the two-stage machine learning bifurcation.
In "https://think-lab.github.io/d/212", there is a text saying "The slim clinical trial catalog contains 6,382 drug–disease pairs", but it does match with the number 5,594. How did you select 5,594 indications?
Good observation. There are 788 clinical trial indications from
See the note in the Disease Modifying indication set description:
Here is the line of code that filters indications based on their existence in PharmacotherapyDB (from this notebook in
Hence, according to the source code, I filtered any of the 1,388 compound–disease pairs that were categorized in PharmacotherapyDB . This includes disease-modifying, symptomatic, and non indications. So of the 1,388 compound–disease pairs in PharmacotherapyDB, 788 had clinical trials.
I will think about how to make the manuscript description more clear. As per the source code, the DrugCentral and Clinical Trial indication sets used to evaluate performance omit all compound–disease pairs in PharmacotherapyDB.