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Drug-target interaction prediction through domain-tuned network-based inference.

Alaimo S, Pulvirenti A, Giugno R, Ferro A - Bioinformatics (2013)

Bottom Line: However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain.DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank.Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Computer Science and Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy.

ABSTRACT

Motivation: The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain.

Results: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.

Availability: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.

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Comparison between DT-Hybrid, Hybrid and NBI by means of receiver operating characteristic (ROC) curves, computed for the top-30 places of the recommendation lists, which were built on the four datasets (enzymes, ion channels, GPCRs and nuclear receptors)
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btt307-F2: Comparison between DT-Hybrid, Hybrid and NBI by means of receiver operating characteristic (ROC) curves, computed for the top-30 places of the recommendation lists, which were built on the four datasets (enzymes, ion channels, GPCRs and nuclear receptors)

Mentions: In this article, we propose a method called DT-Hybrid, which extends NBI (Cheng et al., 2012; Zhou et al., 2007) and the Hybrid (Zhou et al., 2010) algorithms by integrating previous domain-dependent knowledge. Experiments show that this extension improves both algorithms in terms of prediction of new biologically significant interactions. In the supporting materials, we report a comprehensive analysis of DT-Hybrid and Hybrid, together with their behavior varying the α (only for DT-Hybrid) and λ parameters. Table 3 illustrates the result of comparing NBI, Hybrid and DT-Hybrid in terms of precision and recall enhancement. DT-Hybrid clearly outperforms both NBI and Hybrid in recovering deleted links. It is important to point out that hybrid algorithms are able to significantly improve recall (eR) measuring the prediction ability of recovering existing interactions in a complex network. Figure 1 illustrates the receiver operating characteristic (ROC) curves calculated over the complete DrugBank dataset. Simulations were executed 30 times, and the results were averaged to obtain a performance evaluation. Experiments show that all three techniques have a high true-positive rate against a low false-positive rate. However, hybrid algorithms provided better performance than NBI. In particular, Table 3 clearly shows an increase of the average areas under the ROC curves (AUC) in the complete dataset (a detailed analysis can be found in the supporting materials section). This indicates that hybrid algorithms improve the ability of discriminating known links from predicted ones. The increase of the AUC values for the DT-Hybrid algorithm demonstrates that adding biological information to prediction is a key choice to achieve significant results. Table 4 demonstrates that exploiting biological information leads, in most cases, to a significant increase of the adjusted precision and recall. Figure 2 illustrates the ROC curves calculated on the enzymes, ion channels, GPCRs, and nuclear receptor datasets using the top-30 predictions. Finally, it can be asserted that adding similarity makes prediction more reliable than an algorithm, such as NBI, which applies only network topology to score computation. Indeed, using only known interactions of a new structure without any target information makes it impossible to predict new targets for this drug. This weakness is a problem for all methods based on recommendation techniques. The introduction of new biological structures is equivalent to the addition of isolated nodes in the network, whose weight, based on the equation (1), is always zero. Such a weight, ultimately, leads to the impossibility of obtaining a prediction for this new molecule.Fig. 1.


Drug-target interaction prediction through domain-tuned network-based inference.

Alaimo S, Pulvirenti A, Giugno R, Ferro A - Bioinformatics (2013)

Comparison between DT-Hybrid, Hybrid and NBI by means of receiver operating characteristic (ROC) curves, computed for the top-30 places of the recommendation lists, which were built on the four datasets (enzymes, ion channels, GPCRs and nuclear receptors)
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3722516&req=5

btt307-F2: Comparison between DT-Hybrid, Hybrid and NBI by means of receiver operating characteristic (ROC) curves, computed for the top-30 places of the recommendation lists, which were built on the four datasets (enzymes, ion channels, GPCRs and nuclear receptors)
Mentions: In this article, we propose a method called DT-Hybrid, which extends NBI (Cheng et al., 2012; Zhou et al., 2007) and the Hybrid (Zhou et al., 2010) algorithms by integrating previous domain-dependent knowledge. Experiments show that this extension improves both algorithms in terms of prediction of new biologically significant interactions. In the supporting materials, we report a comprehensive analysis of DT-Hybrid and Hybrid, together with their behavior varying the α (only for DT-Hybrid) and λ parameters. Table 3 illustrates the result of comparing NBI, Hybrid and DT-Hybrid in terms of precision and recall enhancement. DT-Hybrid clearly outperforms both NBI and Hybrid in recovering deleted links. It is important to point out that hybrid algorithms are able to significantly improve recall (eR) measuring the prediction ability of recovering existing interactions in a complex network. Figure 1 illustrates the receiver operating characteristic (ROC) curves calculated over the complete DrugBank dataset. Simulations were executed 30 times, and the results were averaged to obtain a performance evaluation. Experiments show that all three techniques have a high true-positive rate against a low false-positive rate. However, hybrid algorithms provided better performance than NBI. In particular, Table 3 clearly shows an increase of the average areas under the ROC curves (AUC) in the complete dataset (a detailed analysis can be found in the supporting materials section). This indicates that hybrid algorithms improve the ability of discriminating known links from predicted ones. The increase of the AUC values for the DT-Hybrid algorithm demonstrates that adding biological information to prediction is a key choice to achieve significant results. Table 4 demonstrates that exploiting biological information leads, in most cases, to a significant increase of the adjusted precision and recall. Figure 2 illustrates the ROC curves calculated on the enzymes, ion channels, GPCRs, and nuclear receptor datasets using the top-30 predictions. Finally, it can be asserted that adding similarity makes prediction more reliable than an algorithm, such as NBI, which applies only network topology to score computation. Indeed, using only known interactions of a new structure without any target information makes it impossible to predict new targets for this drug. This weakness is a problem for all methods based on recommendation techniques. The introduction of new biological structures is equivalent to the addition of isolated nodes in the network, whose weight, based on the equation (1), is always zero. Such a weight, ultimately, leads to the impossibility of obtaining a prediction for this new molecule.Fig. 1.

Bottom Line: However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain.DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank.Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Computer Science and Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy.

ABSTRACT

Motivation: The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do not take into account important features within the drug-target domain.

Results: In this article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.

Availability: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.

Show MeSH