Figures and data in Systematic integration of biomedical knowledge prioritizes drugs for repurposing

Figures
Tables
Additional files

6 figures, 4 tables and 1 additional file

Figures

Figure 1

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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.

https://doi.org/10.7554/eLife.26726.003

Figure 2

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Performance by type and model coefficients.

(A) The performance of the DWPCs for 1206 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 (Himmelstein and Lizee, 2016a).

https://doi.org/10.7554/eLife.26726.006

Figure 3

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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.

https://doi.org/10.7554/eLife.26726.008

Figure 4

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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 (Mihalak et al., 2006). Furthermore, CHRNA3 is associated with nicotine dependence (Thorgeirsson et al., 2008) and participates in several pathways that contain other nicotinic-acetylcholine-receptor (nAChR) genes associated with nicotine dependence. Finally, bupropion causes terminal insomnia (Boshier et al., 2003) as does varenicline (Hays et al., 2008), which could indicate an underlying common mechanism of action.

https://doi.org/10.7554/eLife.26726.009

Figure 5

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Top 100 epilepsy predictions.

(A) Compounds — ranked from 1 to 100 by their predicted probability of treating epilepsy — are colored by their effect on seizures (Khankhanian and Himmelstein, 2016). The highest predictions are almost exclusively anti-ictogenic. Further down the prediction list, the prevalence of drugs with an ictogenic (contraindication) or unknown (novel repurposing candidate) effect on epilepsy increases. All compounds shown received probabilities far exceeding the null probability of treatment (0.36%). (B) A chemical similarity network of the epilepsy predictions, with each compound’s 2D structure (Himmelstein et al., 2017a). Edges are Compound–resembles–Compound relationships from Hetionet v1.0. Nodes are colored by their effect on seizures. (C) The relative contribution of important drug targets to each epilepsy prediction (Himmelstein et al., 2017a). Specifically, pie charts show how the eight most-supportive drug targets across all 100 epilepsy predictions contribute to individual predictions. Other Targets represents the aggregate contribution of all targets not listed. The network layout is identical to B.

https://doi.org/10.7554/eLife.26726.010

Figure 6

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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 Project Rephetio (Himmelstein and Lizee, 2016v). Users are ordered by date of first contribution. Users who contributed over 4500 characters are named. The square root transformation of characters written per user accentuates the activity of new contributors, thereby emphasizing collaboration and diverse input.

https://doi.org/10.7554/eLife.26726.012

Tables

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.

https://doi.org/10.7554/eLife.26726.004

Metanode	Abbr	Nodes	Disconnected	Metaedges
Anatomy	A	402	2	4
Biological process	BP	11,381	0	1
Cellular component	CC	1391	0	1
Compound	C	1552	14	8
Disease	D	137	1	8
Gene	G	20,945	1800	16
Molecular function	MF	2884	0	1
Pathway	PW	1822	0	1
Pharmacologic class	PC	345	0	1
Side effect	SE	5734	33	1
Symptom	S	438	23	1

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.

https://doi.org/10.7554/eLife.26726.005

Metaedge	Abbr	Edges	Sources	Targets
Anatomy–downregulates–Gene	AdG	102,240	36	15,097
Anatomy–expresses–Gene	AeG	526,407	241	18,094
Anatomy–upregulates–Gene	AuG	97,848	36	15,929
Compound–binds–Gene	CbG	11,571	1389	1689
Compound–causes–Side Effect	CcSE	138,944	1071	5701
Compound–downregulates–Gene	CdG	21,102	734	2880
Compound–palliates–Disease	CpD	390	221	50
Compound–resembles–Compound	CrC	6486	1042	1054
Compound–treats–Disease	CtD	755	387	77
Compound–upregulates–Gene	CuG	18,756	703	3247
Disease–associates–Gene	DaG	12,623	134	5392
Disease–downregulates–Gene	DdG	7623	44	5745
Disease–localizes–Anatomy	DlA	3602	133	398
Disease–presents–Symptom	DpS	3357	133	415
Disease–resembles–Disease	DrD	543	112	106
Disease–upregulates–Gene	DuG	7731	44	5630
Gene–covaries–Gene	GcG	61,690	9043	9532
Gene–interacts–Gene	GiG	147,164	9526	14,084
Gene–participates–Biological Process	GpBP	559,504	14,772	11,381
Gene–participates–Cellular Component	GpCC	73,566	10,580	1391
Gene–participates–Molecular Function	GpMF	97,222	13,063	2884
Gene–participates–Pathway	GpPW	84,372	8979	1822
Gene→regulates→Gene	Gr > G	265,672	4634	7048
Pharmacologic Class–includes–Compound	PCiC	1029	345	724

Table 3

The predictiveness of select metapaths.

A small selection of interesting or influential metapaths is provided (complete table online). 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 Materials and 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.

https://doi.org/10.7554/eLife.26726.007

Abbrev.	Len.	Δ auroc	−log₁₀(P)	Coef.	Metapath
CbGaD	2	14.5%	6.2	0.20	Compound–binds–Gene–associates–Disease
CdGuD	2	1.7%	4.5		Compound–downregulates–Gene–upregulates–Disease
CrCtD	2	22.8%	6.9	0.15	Compound–resembles–Compound–treats–Disease
CtDrD	2	17.2%	5.8	0.13	Compound–treats–Disease–resembles–Disease
CuGdD	2	1.1%	2.6		Compound–upregulates–Gene–downregulates–Disease
CbGbCtD	3	21.7%	6.5	0.22	Compound–binds–Gene–binds–Compound–treats–Disease
CbGeAlD	3	8.4%	5.2	0.04	Compound–binds–Gene–expresses–Anatomy–localizes–Disease
CbGiGaD	3	9.0%	4.4	0.00	Compound–binds–Gene–interacts–Gene–associates–Disease
CcSEcCtD	3	14.0%	6.8	0.08	Compound–causes–Side Effect–causes–Compound–treats–Disease
CdGdCtD	3	3.8%	4.6	0.00	Compound–downregulates–Gene–downregulates–Compound–treats–Disease
CdGuCtD	3	−2.1%	2.4		Compound–downregulates–Gene–upregulates–Compound–treats–Disease
CiPCiCtD	3	23.3%	7.5	0.16	Compound–includes–Pharmacologic Class–includes–Compound–treats–Disease
CpDpCtD	3	4.3%	3.9	0.06	Compound–palliates–Disease–palliates–Compound–treats–Disease
CrCrCtD	3	17.0%	5.0	0.12	Compound–resembles–Compound–resembles–Compound–treats–Disease
CrCbGaD	3	8.2%	6.1	0.002	Compound–resembles–Compound–binds–Gene–associates–Disease
CtDdGdD	3	4.2%	3.9		Compound–treats–Disease–downregulates–Gene–downregulates–Disease
CtDdGuD	3	0.5%	1.0		Compound–treats–Disease–downregulates–Gene–upregulates–Disease
CtDlAlD	3	12.4%	6.0		Compound–treats–Disease–localizes–Anatomy–localizes–Disease
CtDpSpD	3	13.9%	6.1		Compound–treats–Disease–presents–Symptom–presents–Disease
CtDuGdD	3	0.7%	1.3		Compound–treats–Disease–upregulates–Gene–downregulates–Disease
CtDuGuD	3	1.1%	1.4		Compound–treats–Disease–upregulates–Gene–upregulates–Disease
CuGdCtD	3	−1.6%	2.9		Compound–upregulates–Gene–downregulates–Compound–treats–Disease
CuGuCtD	3	4.4%	3.5	0.00	Compound–upregulates–Gene–upregulates–Compound–treats–Disease
CbGiGiGaD	4	7.0%	5.1	0.00	Compound–binds–Gene–interacts–Gene–interacts–Gene–associates–Disease
CbGpBPpGaD	4	4.9%	3.8	0.00	Compound–binds–Gene–participates–Biological Process–participates–Gene–associates–Disease
CbGpPWpGaD	4	7.6%	7.9	0.05	Compound–binds–Gene–participates–Pathway–participates–Gene–associates–Disease

Table 4

The 29 public data resources integrated to construct Hetionet v1.0.

Components notes which types of nodes and edges in Hetionet v1.0 derived from the resource (as per the abbreviations in Table 1 and 2). Cat. notes the general category of license (Himmelstein et al., 2015i). Category 1 refers to United States government works that we deemed were not subject to copyright. Category 2 refers to resources with licenses that allow use, redistribution, and modification (although some restrictions may still exist). The subset of category 2 licenses that we deemed to meet the the Open Definition are denoted with ^OD. Category 4 refers to resources without a license, hence with all rights reserved. References provides Research Resource Identifiers as well as citations to resource publications and related Project Rephetio materials. For information on license provenance, institutional affiliations, and funding for each resource, see the online table.

https://doi.org/10.7554/eLife.26726.011

Resource	Components	License	Cat.	References
Entrez Gene	G	custom	1	RRID:SCR_002473 (Maglott et al., 2011; Himmelstein et al., 2015h; Himmelstein, 2016l)
LabeledIn	CtD, CpD	custom	1	RRID:SCR_015667 (Khare et al., 2014; Khare et al., 2015; Himmelstein and Khare, 2015s)
MEDLINE	DlA, DpS, DrD	custom	1	RRID:SCR_002185 (Himmelstein and Pankov, 2015a; Himmelstein, 2016u)
MeSH	S	custom	1	RRID:SCR_004750 (Himmelstein and Pankov, 2015a; Himmelstein, 2016h)
Pathway Interaction Database	PW, GpPW		1	RRID:SCR_006866 (Schaefer et al., 2009; Pico and Himmelstein, 2015; Himmelstein and Pico, 2016a)
Disease Ontology	D	CC BY 3.0	2^OD	RRID:SCR_000476 (Schriml et al., 2012; Kibbe et al., 2015; Himmelstein and Li, 2015d; Himmelstein, 2016g)
DISEASES	DaG	CC BY 4.0	2^OD	RRID:SCR_015664 (Himmelstein and Jensen, 2015l; Himmelstein and Jensen, 2016c; Pletscher-Frankild et al., 2015)
DrugCentral	PC, CbG, PCiC	CC BY 4.0	2^OD	RRID:SCR_015663 (Ursu et al., 2017; Himmelstein et al., 2016d)
Gene Ontology	BP, CC, MF, GpBP, GpCC, GpMF	CC BY 4.0	2^OD	RRID:SCR_002811 (Ashburner et al., 2000; Huntley et al., 2015; Himmelstein et al., 2015g; Himmelstein et al., 2015f)
GWAS Catalog	DaG	custom	2^OD	RRID:SCR_012745 (Himmelstein and Baranzini, 2016b; MacArthur et al., 2017; Himmelstein, 2015h; Himmelstein et al., 2015v)
Reactome	PW, GpPW	custom	2^OD	RRID:SCR_003485 (Fabregat et al., 2016; Cerami et al., 2011; Pico and Himmelstein, 2015; Himmelstein and Pico, 2016a)
LINCS L1000	CdG, CuG, Gr > G	custom	2^OD	(Himmelstein and Chung, 2015q; Himmelstein et al., 2016k; Himmelstein, 2015k)
TISSUES	AeG	CC BY 4.0	2^OD	RRID:SCR_015665 (Santos et al., 2015; Himmelstein and Jensen, 2015g; Himmelstein and Jensen, 2015h)
Uberon	A	CC BY 3.0	2^OD	RRID:SCR_010668 (Mungall et al., 2012; Malladi et al., 2015; Himmelstein, 2016m)
WikiPathways	PW, GpPW	CC BY 3.0/custom	2^OD	RRID:SCR_002134 (Kutmon et al., 2016; Pico et al., 2008; Pico and Himmelstein, 2015; Himmelstein and Pico, 2016a)
BindingDB	CbG	mixed CC BY 3.0 and CC BY-SA 3.0	2^OD	RRID:SCR_000390 (Chen et al., 2001; Gilson et al., 2016; Himmelstein and Gilson, 2015i; Himmelstein et al., 2015d)
DisGeNET	DaG	ODbL	2^OD	RRID:SCR_006178 (Himmelstein, 2015f; Himmelstein and Piñero, 2016d; Piñero et al., 2015; Piñero et al., 2017)
DrugBank	C, CbG, CrC	custom	2	RRID:SCR_002700 (Law et al., 2014; Himmelstein, 2015b; Himmelstein, 2016i; Himmelstein et al., 2016r)
MEDI	CtD, CpD	CC BY-NC-SA 3.0	2	RRID:SCR_015668 (Himmelstein et al., 2015e; Wei et al., 2013)
PREDICT	CtD, CpD	CC BY-NC-SA 3.0	2	(Gottlieb et al., 2011; Himmelstein et al., 2015e)
SIDER	SE, CcSE	CC BY-NC-SA 4.0	2	RRID:SCR_004321 (Kuhn et al., 2016; Himmelstein, 2015c; Himmelstein, 2016j)
Bgee	AeG, AdG, AuG		4	RRID:SCR_002028 (Himmelstein et al., 2016f; Himmelstein and Bastian, 2015e; Himmelstein and Bastian, 2015f; Bastian et al., 2008)
DOAF	DaG		4	RRID:SCR_015666 (Himmelstein, 2015g; Himmelstein, 2016s; Xu et al., 2012)
ehrlink	CtD, CpD		4	(McCoy et al., 2012; Himmelstein, 2015j)
Evolutionary Rate Covariation	GcG		4	RRID:SCR_015669 (Priedigkeit et al., 2015; Himmelstein and Partha, 2015r; Himmelstein, 2016w)
hetio-dag	GiG		4	(Himmelstein and Baranzini, 2015a; Himmelstein et al., 2015z; Himmelstein and Baranzini, 2016e)
Incomplete Interactome	GiG		4	(Himmelstein et al., 2015z; Himmelstein and Baranzini, 2016e; Menche et al., 2015; Himmelstein, 2015a)
Human Interactome Database	GiG		4	RRID:SCR_015670 (Himmelstein et al., 2015z; Himmelstein and Baranzini, 2016e; Rual et al., 2005; Venkatesan et al., 2009; Yu et al., 2011; Rolland et al., 2014)
STARGEO	DdG, DuG		4	(Himmelstein et al., 2015a; Himmelstein et al., 2016j; Hadley et al., 2017)