Sub-network of Adamon.

<p>Each node represents a drug. Drugs approved for the treatment of Parkinson 's disease are marked in orange. Drugs approved for pain treatment are marked in blue.</p

Effect of corrosion defect length on collapse pressure under varied tension (D/t = 20).

Effect of corrosion defect length on collapse pressure under varied tension (D/t = 20).</p

Collapse pressure vs. corrosion angle with various tensions.

<p>Collapse pressure vs. corrosion angle with various tensions.</p

Sub-network of Dynastat.

<p>Each node represents a drug. Drugs approved for pain management are marked in yellow. Drugs approved for rheumatoid arthritis therapy are marked in purple.</p

Construction of Drug Network Based on Side Effects and Its Application for Drug Repositioning

<div><p>Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indications for 1234 drugs with more than 2 network neighbours, 36.87% of the drugs achieved a performance score of <b>N</b>ormalized <b>D</b>iscounted <b>C</b>umulative <b>G</b>ain in the top <b>5</b> positions (NDCG@5)≥0.7, which means most of the known FDA-approved indications were well predicted at the top 5 positions. In particular, drugs for diabetes, obesity, laxatives and antimycobacterials had extremely high performance with more than 80% of them achieving NDCG@5≥0.7. Additionally, by manually checking the predicted 1858 drug-indication pairs with <b>E</b>xpression <b>A</b>nalysis <b>S</b>ystematic <b>E</b>xplorer (EASE) score≤10⁻⁵ (EASE score is a rigorously modified Fisher exact test p value), we found that 80.73% of such pairs could be verified by preclinical/clinical studies or scientific literature. Furthermore, our method could be extended to predict drugs not covered in the network. We took 98 external drugs not covered in the network as the test sample set. Based on our similarity criteria using side effects, we identified 41 drugs with significant similarities to other drugs in the network. Among them, 36.59% of the drugs achieved NDCG@5≥0.7. In all of the 106 drug-indication pairs with an EASE score≤0.05, 50.94% of them are supported by FDA approval or preclinical/clinical studies. In summary, our method which is based on the indications enriched by network neighbors may provide new clues for drug repositioning using side effects.</p></div

Sub-network of Tasmar.

<p>Each node represents a drug. Drugs approved for the treatment of Parkinson's disease are marked in orange. Drugs approved for rheumatoid arthritis therapy are marked in blue.</p

The trends of covered drugs and drug-drug pairs that share the same terms in the MeSH hierarchy.

<p>The trends of covered drugs and drug-drug pairs that share the same terms in the MeSH hierarchy.</p

Finite element model of pipelines with asymmetric initial ovality imperfection.

<p>Finite element model of pipelines with asymmetric initial ovality imperfection.</p

Prediction performance of the 1234 drugs in different indication range (number of network drugs approved for an indication).

<p>Prediction performance of the 1234 drugs in different indication range (number of network drugs approved for an indication).</p

Drug indication resources.

<p>Drug indication resources.</p