offsides_twosides — OFFSIDES and TWOSIDES drug side effects and interactions (FAERS)
- Description
Networks of drug side effects and of side effects of drug combinations, from OFFSIDES and TWOSIDES of the Tatonetti lab (nSIDES project), mined from the reports of adverse events of the FDA Adverse Event Reporting System (FAERS). For each drug (or pair of drugs) and each adverse event, the proportional reporting ratio (PRR) compares the frequency of the event among the reports of the drug with its frequency among the reports of control drugs, which are matched by a propensity score model to correct for confounding (e.g. by indication or co-medication). OFFSIDES focuses on effects that are not listed on the drug label ("off-label" side effects). The files repeat their header line every 200,000 rows; these lines are skipped. The data are those of the files published in March 2024 (from the release of November 2019), which their authors describe as out of date.
offsides: bipartite network of drugs and adverse events (side effects), where an edge is a signal of the drug causing the event.twosides: network of drugs, where an edge joins two drugs whose combination is associated with an adverse event, labeled by the event; two drugs are joined by one edge per event (so that the network is a multigraph).
The drugs are RxNorm ingredients, and the adverse events are MedDRA terms (preferred terms).
The networks have the following properties:
- (Node)
rxnorm_id: the RxNorm identifier of the drug (-1 for adverse events). - (Node)
meddra_id(offsides): the MedDRA identifier of the adverse event (-1 for drugs). - (Node)
name: the name of the drug or of the adverse event. - (Node)
is_drug(offsides): whether the node is a drug (otherwise it is an adverse event). - (Edge)
condition_meddra_id,condition_name(twosides): the MedDRA identifier and name of the adverse event. - (Edge)
a: the number of reports for the drug (or pair of drugs) that report the event;b: the number of its reports that do not report it;c: the number of reports of the matched control drugs that report the event;d: the number of reports of the control drugs that do not. - (Edge)
prr: the proportional reporting ratio, $(a/(a+b))/(c/(c+d))$, andprr_errorits error estimate (NaN if not given; the counts are -1 if not given). - (Edge)
mean_reporting_frequency: the fraction of the reports of the drug (or pair) that report the event, $a/(a+b)$.
- Tags
- Biological Drug interactions Public Health Weighted Multigraph Metadata
- Added
- 2026-10-31
- Citation
-
- N. P. Tatonetti, P. P. Ye, R. Daneshjou, R. B. Altman, "Data-Driven Prediction of Drug Effects and Interactions." Science Translational Medicine 4, 125ra31 (2012) 10.1126/scitranslmed.3003377 Sci-Hub
- Upstream URL OK
-
https://nsides.io/
https://tatonettilab-resources.s3.amazonaws.com/nsides/README.txt - Networks
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Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv offsides 17,272 3,206,557 371.30 878.53 1266.38 1.61 -0.39 0.00 5 1.00 Undirected Bipartite rxnorm_id meddra_id name is_drug a b c d prr prr_error mean_reporting_frequency 28.0 MiB 38.4 MiB 36.5 MiB 36.8 MiB twosides 1,916 42,920,390 44802.08 99462.86 502.03 2.83 -0.14 0.86 5 1.00 Undirected Unipartite rxnorm_id name condition_meddra_id condition_name a b c d prr prr_error mean_reporting_frequency 323.0 MiB 585.5 MiB 557.8 MiB 647.2 MiB - Ridiculograms*