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econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence.

08:00 EDT 9th September 2019 | BioPortfolio

Summary of "econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence."

RNA binding proteins (RBPs) determine RNA process from synthesis to decay, which play a key role in RNA transport, translation and degradation. Therefore, exploring RBPs' function from the amino acid sequence using computational methods has become one of the momentous topics in genome annotation. However, there still have some challenges: (1) shallow feature: Although the sequence determines structure is self-evident, it is difficult to analyze the essential features from simple sequence. (2) Poorly understand: feature-based prediction methods mainly emphasize feature extraction, while in-depth understanding of protein mysteries limits the application of feature engineering. (3) Feature fusion: multi-feature fusion is often used, but the features are not well integrated. In view of these challenges, we propose a novel ensemble convolutional neural network (econvRBP) to predict RBPs. In order to capture the local and global features of RNA binding proteins simultaneously, first of all, One Hot and Conjoint Triad encoding methods are used to transform amino acid sequence into local and global features, respectively. After that the local and global features are combined for further high-level feature extraction using convolutional neural networks. Some experiments are constructed to evaluate our method with 10-fold cross validation and the results show that it has achieved the best performance among all the predictors so far. We correctly predicted 99% of 2875 RBPs and 99% of 6782 non-RBPs with accuracy of 0.99. In addition, the datasets provided by RBPPred are also used to validate our models with an accuracy of 0.87. These results indicate that the econvRBP is the most excellent method at present, and will provide reliable guidance for the detection of RBPs. econvRBP is available at http http://47.100.203.218:3389/home.html/.

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This article was published in the following journal.

Name: Methods (San Diego, Calif.)
ISSN: 1095-9130
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A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.

A neural-specific RRM protein that binds to several 3'UTRs, including its own as well as that of PROTO-ONCOGENE PROTEINS C-FOS and ID DNA BINDING PROTEIN INHIBITOR. It binds through the recognition of a GAAA motif in the RNAs.

GTP-BINDING PROTEINS that contain three non-identical subunits. They are found associated with members of the seven transmembrane domain superfamily of G-PROTEIN-COUPLED RECEPTORS. Upon activation the GTP-BINDING PROTEIN ALPHA SUBUNIT of the complex dissociates leaving a dimer of a GTP-BINDING PROTEIN BETA SUBUNIT bound to a GTP-BINDING PROTEIN GAMMA SUBUNIT.

A poly(A) binding protein that has a variety of functions such as mRNA stabilization and protection of RNA from nuclease activity. Although poly(A) binding protein I is considered a major cytoplasmic RNA-binding protein it is also found in the CELL NUCLEUS and may be involved in transport of mRNP particles.

A member of the p300-CBP transcription factor family that was initially identified as a binding partner for CAMP RESPONSE ELEMENT-BINDING PROTEIN. Mutations in CREB-binding protein are associated with RUBINSTEIN-TAYBI SYNDROME.

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