Dataset Information

Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier.

ABSTRACT: Accumulated evidence has shown that microRNAs (miRNAs) can functionally interact with a number of environmental factors (EFs) and their interactions critically affect phenotypes and diseases. Therefore, in-silico inference of disease-related miRNA-EF interactions is becoming crucial not only for the understanding of the mechanisms by which miRNAs and EFs contribute to disease, but also for disease diagnosis, treatment, and prognosis. In this paper, we analyzed the human miRNA-EF interaction data and revealed that miRNAs (EFs) with similar functions tend to interact with similar EFs (miRNAs) in the context of a given disease, which suggests a potential way to expand the current relation space of miRNAs, EFs, and diseases. Based on this observation, we further proposed a semi-supervised classifier based method (miREFScan) to predict novel disease-related interactions between miRNAs and EFs. As a result, the leave-one-out cross validation has shown that miREFScan obtained an AUC of 0.9564, indicating that miREFScan has a reliable performance. Moreover, we applied miREFScan to predict acute promyelocytic leukemia-related miRNA-EF interactions. The result shows that forty-nine of the top 1% predictions have been confirmed by experimental literature. In addition, using miREFScan we predicted and publicly released novel miRNA-EF interactions for 97 human diseases. Finally, we believe that miREFScan would be a useful bioinformatic resource for the research about the relationships among miRNAs, EFs, and human diseases.

SUBMITTER: Chen X

PROVIDER: S-EPMC3427386 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier.

Chen Xing X Liu Ming-Xi MX Cui Qing-Hua QH Yan Gui-Ying GY

PloS one 20120824 8

Accumulated evidence has shown that microRNAs (miRNAs) can functionally interact with a number of environmental factors (EFs) and their interactions critically affect phenotypes and diseases. Therefore, in-silico inference of disease-related miRNA-EF interactions is becoming crucial not only for the understanding of the mechanisms by which miRNAs and EFs contribute to disease, but also for disease diagnosis, treatment, and prognosis. In this paper, we analyzed the human miRNA-EF interaction data ...[more]

PMID: 22937049

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Project description:Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2-domains, freely available to the scientific community under the following URLs: http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/SH2PepInt.tar.gz and http://www.bioinf.uni-freiburg.de/Software/SH2PepInt/Genome-wide-predictions.tar.gz, respectively.

Project description:BackgroundViral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics.MethodsIn this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques.ResultsOut of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus "C protein" binds to membrane docking protein, while "X protein" and "P protein" interacts with cell-killing and metabolic process proteins, respectively.ConclusionThe proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model.

Dataset Information

Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier.

Publications

Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier.

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