Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., "orphan" nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the "new drug" and "new target" cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some "new drug" and "new target" cases and found that GRMF predicted the left-out interactions reasonably well.

Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.

BackgroundMultiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types.ResultsWe propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully.ConclusionsOur proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1377-y) contains supplementary material, which is available to authorized users.

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a b s t r a c tDynamics of acoustically driven bubbles' radial oscillations in viscoelastic fluids are known as complex and uncontrollable phenomenon indicative of highly active nonlinear as well as chaotic behavior. In the present paper, the effect of magnetic fields on the non-linear behavior of bubble growth under the excitation of an acoustic pressure pulse in non-Newtonian fluid domain has been investigated. The constitutive equation [Upper-Convective Maxwell (UCM)] was used for modeling the rheological behaviors of the fluid. Due to the importance of the bubble in the medical applications such as drug, protein or gene delivery, blood is assumed to be the reference fluid. It was found that the magnetic field parameter (B) can be used for controlling the nonlinear radial oscillations of a spherical, acoustically forced gas bubble in nonlinear viscoelastic media. The relevance and importance of this control method to biomedical ultrasound applications were highlighted. We have studied the dynamic behavior of the radial response of the bubble before and after applying the magnetic field using Lyapunov exponent spectra, bifurcation diagrams and time series. A period-doubling bifurcation structure was predicted to occur for certain values of the parameters effects. Results indicated its strong impact on reducing the chaotic radial oscillations to regular ones.

In this paper, a group of structure-based algorithms used in the prediction of ligand binding sites in proteins were tested on apo-structures, and their results are analyzed and discussed. In particular, we focused on the accuracy of predicting the residues at the binding sites as the main criterion for comparison. The data set used for the testing was compiled using the LigASite database and consisted entirely of apo-structures. The tools compared are LIGSITE, SURFNET, DEPTH, GHECOM, SiteHound, and Fpocket.

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