Prediction of pre-miRNA with multiple stem-loops using pruning algorithm

Song, Xiaofeng; Wang, Minghao; Chen, Yi‐Ping Phoebe; Wang, Huating; Han, Ping; Sun, Hao

doi:10.1016/j.compbiomed.2013.02.003

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…The SVM method has proven to be powerful in many fields of bioinformatics [18,19,20,51,52]. In this study, the SVM was trained with the LIBSVM package [53] to build the model and perform the predictions.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition

Jia

Lin

Wang

2014

IJMS

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Protein S-nitrosylation is a reversible post-translational modification by covalent modification on the thiol group of cysteine residues by nitric oxide. Growing evidence shows that protein S-nitrosylation plays an important role in normal cellular function as well as in various pathophysiologic conditions. Because of the inherent chemical instability of the S-NO bond and the low abundance of endogenous S-nitrosylated proteins, the unambiguous identification of S-nitrosylation sites by commonly used proteomic approaches remains challenging. Therefore, computational prediction of S-nitrosylation sites has been considered as a powerful auxiliary tool. In this work, we mainly adopted an adapted normal distribution bi-profile Bayes (ANBPB) feature extraction model to characterize the distinction of position-specific amino acids in 784 S-nitrosylated and 1568 non-S-nitrosylated peptide sequences. We developed a support vector machine prediction model, iSNO-ANBPB, by incorporating ANBPB with the Chou’s pseudo amino acid composition. In jackknife cross-validation experiments, iSNO-ANBPB yielded an accuracy of 65.39% and a Matthew’s correlation coefficient (MCC) of 0.3014. When tested on an independent dataset, iSNO-ANBPB achieved an accuracy of 63.41% and a MCC of 0.2984, which are much higher than the values achieved by the existing predictors SNOSite, iSNO-PseAAC, the Li et al. algorithm, and iSNO-AAPair. On another training dataset, iSNO-ANBPB also outperformed GPS-SNO and iSNO-PseAAC in the 10-fold crossvalidation test.

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Section: Methodsmentioning

confidence: 99%

Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition

Jia

Lin

Wang

2014

IJMS

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“…Through the analysis of miRNAs and TFs linking to high ranking hubs, the authors determined some “key players” including miRNAs and TFs in ovarian cancer. Tacutu et al [78,106] identified 35 genes related to aging related diseases (ARDs) from the miRNA-regulated PPI networks which they constructed from miRNA-regulated genes and the Common Gene Signature (in [78,107-109]; CGS denotes a PPI network based on the overlap between the PPI Human Longevity Network-http://www.netage-project.org[106] and human aging-related disease PPI networks).…”

Section: Reviewmentioning

confidence: 99%

Computational developments in microRNA-regulated protein-protein interactions

Zhu

Chen

2014

BMC Systems Biology

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Protein-protein interaction (PPI) is one of the most important functional components of a living cell. Recently, researchers have been interested in investigating the correlation between PPI and microRNA, which has been found to be a regulator at the post-transcriptional level. Studies on miRNA-regulated PPI networks will not only facilitate an understanding of the fine tuning role that miRNAs play in PPI networks, but will also provide potential candidates for tumor diagnosis. This review describes basic studies on the miRNA-regulated PPI network in the way of bioinformatics which includes constructing a miRNA-target protein network, describing the features of miRNA-regulated PPI networks and overviewing previous findings based on analysing miRNA-regulated PPI network features.

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“…The SVM method has proven to be powerful in many fields of bioinformatics. 6,7,[10][11][12] In this study, the SVM was trained with the LIBSVM package 13 to build the model and perform the predictions. The radial basis kernel function k(x i ,x j ) = exp{ÀgJx i À x j J 2 } was used for our SVM method.…”

Section: Supporting Vector Machine Implementation and Parameter Selec...mentioning

confidence: 99%

O-GlcNAcPRED: a sensitive predictor to capture protein O-GlcNAcylation sites

2013

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O-GlcNAcylation is a ubiquitous post-translational modification of proteins that is involved in the majority of cellular processes and is associated with many diseases. To reduce the workload and increase the relevance of experimental identification of protein O-GlcNAcylation sites, O-GlcNAcPRED, a support vector machine (SVM)-based model, was developed to capture potential O-GlcNAcylation sites. By virtue of the novel adapted normal distribution bi-profile Bayes (ANBPB) feature extraction method, O-GlcNAcPRED yielded a sensitivity of 80.83%, a specificity of 78.17% and an accuracy of 79.50% in jackknife cross-validation experiments. In an independent test on 38 recently experimentally identified human O-GlcNAcylated proteins with 67 O-GlcNAcylation sites, O-GlcNAcPRED captured 26 proteins and 39 sites, clearly outperforming the existing predictors, YinOYang and O-GlcNAcscan.

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Prediction of pre-miRNA with multiple stem-loops using pruning algorithm

Cited by 13 publications

References 30 publications

Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition

Prediction of Protein S-Nitrosylation Sites Based on Adapted Normal Distribution Bi-Profile Bayes and Chou’s Pseudo Amino Acid Composition

Computational developments in microRNA-regulated protein-protein interactions

O-GlcNAcPRED: a sensitive predictor to capture protein O-GlcNAcylation sites

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