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Similarity-based methods for potential human microRNA-disease association prediction.

ABSTRACT: BACKGROUND: The identification of microRNA-disease associations is critical for understanding the molecular mechanisms of diseases. However, experimental determination of associations between microRNAs and diseases remains challenging. Meanwhile, target diseases need to be revealed for some new microRNAs without any known target disease association information as new microRNAs are discovered each year. Therefore, computational methods for microRNA-disease association prediction have gained a lot of research interest. METHODS: Herein, based on the assumption that functionally related microRNAs tend to be associated with phenotypically similar diseases, three inference methods were presented for microRNA-disease association prediction, namely MBSI (microRNA-based similarity inference), PBSI (phenotype-based similarity inference) and NetCBI (network-consistency-based inference). Global network similarity measure was used in the three methods to predict new microRNA-disease associations. RESULTS: We tested the three methods on 242 known microRNA-disease associations by leave-one-out cross-validation for prediction evaluation, and achieved AUC values of 74.83%, 54.02% and 80.66%, respectively. The best-performed method NetCBI was then chosen for novel microRNA-disease association prediction. Some associations strongly predicted by NetCBI were confirmed by the publicly accessible databases, which indicated the usefulness of this method. The newly predicted associations were publicly released to facilitate future studies. Moreover, NetCBI was especially applicable to predicting target diseases for microRNAs whose target association information was not available. CONCLUSIONS: The encouraging results suggest that our method NetCBI can not only provide help in identifying novel microRNA-disease associations but also guide biological experiments for scientific research.

SUBMITTER: Chen H

PROVIDER: S-EPMC3629999 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Similarity-based methods for potential human microRNA-disease association prediction.

Chen Hailin H Zhang Zuping Z

BMC medical genomics 20130409

<h4>Background</h4>The identification of microRNA-disease associations is critical for understanding the molecular mechanisms of diseases. However, experimental determination of associations between microRNAs and diseases remains challenging. Meanwhile, target diseases need to be revealed for some new microRNAs without any known target disease association information as new microRNAs are discovered each year. Therefore, computational methods for microRNA-disease association prediction have gaine ...[more]

PMID: 23570623

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Project description:The prediction of biological targets of bioactive molecules from machine-readable materials can be routinely performed by computational target prediction tools (CTPTs). However, the prediction of biological targets of bioactive molecules from non-digital materials (e.g., printed or handwritten documents) has not been possible due to the complex nature of bioactive molecules and impossibility of employing computations. Improving the target prediction accuracy is the most important challenge for computational target prediction. A minimum structure is identified for each group of neighbor molecules in the proposed method. Each group of neighbor molecules represents a distinct structural class of molecules with the same function in relation to the target. The minimum structure is employed as a query to search for molecules that perfectly satisfy the minimum structure of what is guessed crucial for the targeted activity. The proposed method is based on chemical similarity, but only molecules that perfectly satisfy the minimum structure are considered. Structurally related bioactive molecules found with the same minimum structure were considered as neighbor molecules of the query molecule. The known target of the neighbor molecule is used as a reference for predicting the target of the neighbor molecule with an unknown target. A lot of information is needed to identify the minimum structure, because it is necessary to know which part(s) of the bioactive molecule determines the precise target or targets responsible for the observed phenotype. Therefore, the predicted target based on the minimum structure without employing the statistical significance is considered as a reliable prediction. Since only molecules that perfectly (and not partly) satisfy the minimum structure are considered, the minimum structure can be used without similarity calculations in non-digital materials and with similarity calculations (perfect similarity) in machine-readable materials. Nine tools (PASS online, PPB, SEA, TargetHunter, PharmMapper, ChemProt, HitPick, SuperPred, and SPiDER), which can be used for computational target prediction, are compared with the proposed method for 550 target predictions. The proposed method, SEA, PPB, and PASS online, showed the best quality and quantity for the accurate predictions.

Dataset Information

Similarity-based methods for potential human microRNA-disease association prediction.

Publications

Similarity-based methods for potential human microRNA-disease association prediction.

Similar Datasets

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