drug target prediction using semantic linked data
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beyond data integration, what else can we do? this presentation shows how we use semantic linked data for drug target prediction.
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drug target prediction using semantic linked data
- 1. DRUG TARGET PREDICTION USING SEMANTIC LINKED DATA Bin Chen Ph.D. candidate School of Informatics and Computing, Indiana University binchen@indiana.edu http://cheminfo.informatics.indiana.edu/~binchen CSHALS, Feb 23, 2012
- 4. Why Drug Target Prediction?
- 5. How to predict drug target?
- 6. ? Drug 1 Target 1 •Substructure •Side effect •Chemical ontology •Gene expression profile bind Drug 2 From Ligand (drug) perspective
- 7. ? Drug 1 Target 1 •Sequence bind •3D structure •Gene Ontology •Ligand Target 2 From target perspective
- 8. Troglitazon e Chemical ontology bind bind hypoglycemic ACSL4 drug pathway PPARA bind PPAR GO Chemical ontology signaling bind Eicosapentaen pathway Chemical ontology oic Acid Rosiglitazo Response to ne Pioglitazon nutrient bind e GO pathway bind bind PPARG
- 9. troglitazone troglitazone Semantic Linked Network
- 10. Topology is important for association hasSubstructure hasSubstructure bind Cmpd 1 Cmpd 2 Protein 1 hasSubstructure hasSubstructure bind Cmpd 1 Cmpd 2 Protein 1
- 11. Semantic is important for association Cmpd bind Protein bind bind Protein Cmpd 2 1 2 1 bind hasGO hasGO Protein GO:000 Protein Cmpd1 2 01 1 bind Protein PPI Protein Cmpd1 2 1 hasSideeffect hyperten hasSide ffect bind Protein Cmpd1 Cmpd 2 sion 1 substruc Protein Cmpd1 hasSubstructure hasSubstructure Cmpd 2 bind ture1 1
- 12. (Semantic Link Association Prediction)
- 13. Data schema
- 14. Path finding Drug: Troglitazone >300k nodes, >1million edges Target :PPARG
- 15. Statistical Model---edge weight bind PPI hasPathway P(i j) =1/3 Target Target I PPI J hasGO PPI hasGO
- 16. Statistical Model---path raw score Cmpd1 e1 Target e2 Target (s) e4 2 e3 1 (t)
- 17. Path pattern Path examples: Protei Cmpd Protei bind bind Cmpd bind n 1 n2 1 1 Protei Cmpd Protei bind bind Cmpd bind n 3 n1 4 5 Path pattern: Protei Protei Cmpd bind bind bind n Cmpd n
- 18. Statistical Model---path z score • Randomly sample 100,000 drug target pairs, yielding 453,087 paths, 35 patterns •Plot Path pattern score distribution •Convert path score to path z score Path Pattern Score Distribution
- 19. Statistical Model---association score Fit association scores of random pairs to normal distribution
- 20. Association Score distribution among different pairs
- 21. Comparing SLAP with link prediction methods in social science AUC=0.92 ROC curve
- 22. Drug polypharmacology profiles Bisoprolol Nadolol Acebutolol Alprenolol Betaxolol Trichlormethiazide Metolazone Chlorthalidone Enalapril Trandolapril Fosinopril Quinapril Benazepril Rescinnamine Moexipril Polypharmacology profile comprises of association scores of one drug against over one thousand targets
- 23. Drug similarity network •Nodes present drugs •Two nodes are linked if they are similar in terms of biological function. •Nodes are colored by their therapeutic indications
- 24. Dissimilar Drugs have same indication Insomnia related drugs
- 25. Drug repurposing allergic rhinitis ? Anti-Parkinson http://www.ebi.ac.uk/chebi/searchId.do?chebiId=3398
- 26. Summary Semantic Link association can be assessed by topology and semantics of the network Domain knowledge plays an important role!
- 27. Team Prof. Ying Ding Prof. David Wild
- 29. Thanks!
- 30. Backup slides
- 31. SLAP Pipeline Path filtering
- 32. Chem2Bio2RDF data Other data venders compound protein/gene chemogenomics literature others Chem2Bio2RDF Datasets Chen, B., Dong. X., Jiao, D., Wang, H., Zhu, Q., Ding, Y., Wild, D.J. Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems http://chem2bio2rdf.org chemical biology data. BMC Bioinformatics, 2010, 11:255
- 33. Ranking Target associated chemicals Randomly select three targets Select all target associated chemicals as positive link Randomly select equal number of chemicals that are not associated with the target Compare with Naïve bayes using Molecular Weight, ALogP, number of hydrogen bond acceptors and donors, the number of rotatable bonds and FCFP_6 as descriptors Leave one out validation
- 34. Two objects are related if they are related to same objects Coauthorship Same Target
- 35. Two objects are related if their related objects are related
- 38. Similar Drugs have distinct indications Levodopa: Methyldopa : dopaminergic agent antiadrenergic Anti-parkinson drug Antihypertensive drug Slap similarity: p value>0.05 Tanimoto coefficient=0.89
- 39. Association Score distribution among different pairs Direct: drug target interacts with each other physically Indirect: indirect interaction (e.g., change gene expression) Random: random drug target pairs
Editor's Notes
- We spent two years to integrate data pertaining to drugs, protein, disease, pathways, tissues and so on, into semantic format, contructed a huge semantic linked network,well, after data integration, what else can we do? We definitely can find new knowedge from the semantic linked data such as drug target prediction which I am going to show you today. But we really need domain knowledge to understand the data, so let’s start from beginning.
- A drug can physically interaction aprotein, change the function of the protein and further affecting the function of our body, we call the protein as the drug target
- We took 157 drugs from 10 disease areas. For example drug, we could generate their polypharmaoclog profiles, allows us assess the simialrity of drugs using spearman correlation . At the end, we build a drug similarity network. For example, node, edges, color. The network tells us at least three stories. 1) ace inhibitor 2) thiazide diuretic 3) angiotensin II receptor 4) alpha 1 antogonist 5) beta blocker
- Two author similar