iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking

Xiao, Xuan; Min, Jianliang; Wang, Pu; Chou, Kuo‐Chen

doi:10.1371/journal.pone.0072234

Cited by 107 publications

(54 citation statements)

References 102 publications

(113 reference statements)

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“…For example, He et al represented drugs and targets using common chemical functional groups and pseudo amino acid composition, respectively [24], while Yu et al used molecular descriptors that were calculated using the DRAGON package [25] and the PROFEAT web server [26] for drugs and targets, respectively [27]. Other descriptors have also been used such as position-specific scoring matrices [28], 2D molecular fingerprints [29], MACCS fingerprints [30], and domain and PubChem fingerprints [31]. …”

Section: Introductionmentioning

confidence: 99%

Drug-target interaction prediction via class imbalance-aware ensemble learning

et al. 2016

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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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Section: Introductionmentioning

confidence: 99%

Drug-target interaction prediction via class imbalance-aware ensemble learning

et al. 2016

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“…As anticipated in Chou and Shen (2009) and demonstrated in a series of follow-up publications (Fan et al, 2014;GUo et al, 2014;Qiu et al, 2014;Chen et al, 2013;Xiao et al, 2013aXiao et al, , 2013bXu et al, 2013;Min et al, 2013), user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful models, simulated methods, or predictors, we shall make efforts in our future work to provide a webserver for the method presented in this paper.…”

Section: Resultsmentioning

confidence: 94%

Potential non homologous protein targets of mycobacterium tuberculosis H37Rv identified from protein–protein interaction network

Melak

Gakkhar

2014

Journal of Theoretical Biology

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“…According to , we can extract the following information from the j ‐th column of Eq.

true ([], \begin{matrix} a_{1}^{j} \\ a_{2}^{j} \\ b^{j} \end{matrix}) = {({boldB}_{j}^{normalT} {boldB}_{j})}^{- 1} B_{j}^{T} U_{j} (j = 1, 2, \cdot \cdot \cdot, 20)

…”

Section: Methodsmentioning

confidence: 99%

iPhos‐PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory

Qiu

Sun

Xiao

et al. 2016

Molecular Informatics

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Protein phosphorylation plays a critical role in human body by altering the structural conformation of a protein, causing it to become activated/deactivated, or functional modification. Given an uncharacterized protein sequence, can we predict whether it may be phosphorylated or may not? This is no doubt a very meaningful problem for both basic research and drug development. Unfortunately, to our best knowledge, so far no high throughput bioinformatics tool whatsoever has been developed to address such a very basic but important problem due to its extremely complexity and lacking sufficient training data. Here we proposed a predictor called iPhos-PseEvo by (1) incorporating the protein sequence evolutionary information into the general pseudo amino acid composition (PseAAC) via the grey system theory, (2) balancing out the skewed training datasets by the asymmetric bootstrap approach, and (3) constructing an ensemble predictor by fusing an array of individual random forest classifiers thru a voting system. Rigorous jackknife tests have indicated that very promising success rates have been achieved by iPhos-PseEvo even for such a difficult problem. A user-friendly web-server for iPhos-PseEvo has been established at http://www.jci-bioinfo.cn/iPhos-PseEvo, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the formulation and approach presented here can be used to analyze many other problems in protein science as well.

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iGPCR-Drug: A Web Server for Predicting Interaction between GPCRs and Drugs in Cellular Networking

Cited by 107 publications

References 102 publications

Drug-target interaction prediction via class imbalance-aware ensemble learning

Drug-target interaction prediction via class imbalance-aware ensemble learning

Potential non homologous protein targets of mycobacterium tuberculosis H37Rv identified from protein–protein interaction network

iPhos‐PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory

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