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A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.

Oh M, Ahn J, Yoon Y - PLoS ONE (2014)

Bottom Line: The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level.These quantified scores were used as features for the prediction of novel drug-disease associations.Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Gachon University, Seongnam, Korea.

ABSTRACT
The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer's disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer's disease.

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Visualization of potential pathway associated with targets of telmisartan and genes related to Alzheimer’s disease.
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pone-0111668-g008: Visualization of potential pathway associated with targets of telmisartan and genes related to Alzheimer’s disease.

Mentions: We depicted the potential pathway starting from telmisartan to Alzheimer’s disease through a gene regulatory network. In Figure 8, angiotensin II receptor (AGTR1) activates MTG1 to translocate group A genes, which activate the transcription of alpha-2-macroglobulin (A2M). On the other hand, group B genes activated by MTG1 inhibit the transcription of A2M. In other words, the signaling of the angiotensin receptor interrupts the transcription of A2M, which mediates the clearance and degradation of Aβ. The angiotensin II receptor blocker telmisartan may work such that the transcription of A2M is not interrupted. In addition, PPARγ, denoted as PPARG, inhibits the transcription of IL2, which plays a role similar to that of MTG1 in that it partially activates genes in groups A and B.


A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions.

Oh M, Ahn J, Yoon Y - PLoS ONE (2014)

Visualization of potential pathway associated with targets of telmisartan and genes related to Alzheimer’s disease.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214731&req=5

pone-0111668-g008: Visualization of potential pathway associated with targets of telmisartan and genes related to Alzheimer’s disease.
Mentions: We depicted the potential pathway starting from telmisartan to Alzheimer’s disease through a gene regulatory network. In Figure 8, angiotensin II receptor (AGTR1) activates MTG1 to translocate group A genes, which activate the transcription of alpha-2-macroglobulin (A2M). On the other hand, group B genes activated by MTG1 inhibit the transcription of A2M. In other words, the signaling of the angiotensin receptor interrupts the transcription of A2M, which mediates the clearance and degradation of Aβ. The angiotensin II receptor blocker telmisartan may work such that the transcription of A2M is not interrupted. In addition, PPARγ, denoted as PPARG, inhibits the transcription of IL2, which plays a role similar to that of MTG1 in that it partially activates genes in groups A and B.

Bottom Line: The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level.These quantified scores were used as features for the prediction of novel drug-disease associations.Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Gachon University, Seongnam, Korea.

ABSTRACT
The growing number and variety of genetic network datasets increases the feasibility of understanding how drugs and diseases are associated at the molecular level. Properly selected features of the network representations of existing drug-disease associations can be used to infer novel indications of existing drugs. To find new drug-disease associations, we generated an integrative genetic network using combinations of interactions, including protein-protein interactions and gene regulatory network datasets. Within this network, network adjacencies of drug-drug and disease-disease were quantified using a scored path between target sets of them. Furthermore, the common topological module of drugs or diseases was extracted, and thereby the distance between topological drug-module and disease (or disease-module and drug) was quantified. These quantified scores were used as features for the prediction of novel drug-disease associations. Our classifiers using Random Forest, Multilayer Perceptron and C4.5 showed a high specificity and sensitivity (AUC score of 0.855, 0.828 and 0.797 respectively) in predicting novel drug indications, and displayed a better performance than other methods with limited drug and disease properties. Our predictions and current clinical trials overlap significantly across the different phases of drug development. We also identified and visualized the topological modules of predicted drug indications for certain types of cancers, and for Alzheimer's disease. Within the network, those modules show potential pathways that illustrate the mechanisms of new drug indications, including propranolol as a potential anticancer agent and telmisartan as treatment for Alzheimer's disease.

Show MeSH
Related in: MedlinePlus