Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions

Wang, Lei; You, Zhu-Hong; Huang, De-Shuang; Zhou, Fengfeng

doi:10.1109/tcbb.2018.2874267

Cited by 54 publications

(31 citation statements)

References 41 publications

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“…A classifier was evaluated for optimizing its parameters by the 5-fold cross-validation strategy, and its classification performance was calculated by the leave-one-out validation strategy for the comparison with the existing studies. The k-fold cross validation strategy randomly split both the positive and negative datasets into k equally-sized bins and iteratively tested each pair of one positive and one negative bin with the model trained over the other samples (Wang, et al, 2018;Wang, et al, 2016;Zhao, et al, 2018). The final performance metrics were averaged over all the samples.…”

Section: Evaluation Methods Of Performancementioning

confidence: 99%

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

et al. 2019

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Motivation Cell divisions start from replicating the double-stranded DNA, and the DNA replication process needs to be precisely regulated both spatially and temporally. The DNA is replicated starting from the DNA replication origins. A few successful prediction models were generated based on the assumption that the DNA replication origin regions have sequence level features like physicochemical properties significantly different from the other DNA regions. Results This study proposed a feature selection procedure to further refine the classification model of the DNA replication origins. The experimental data demonstrated that as large as 26% improvement in the prediction accuracy may be achieved on the yeast Saccharomyces cerevisiae. Moreover, the prediction accuracies of the DNA replication origins were improved for all the four yeast genomes investigated in this study. Availability and implementation The software sefOri version 1.0 was available at http://www.healthinformaticslab.org/supp/resources.php. An online server was also provided for the convenience of the users, and its web link may be found in the above-mentioned web page. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Evaluation Methods Of Performancementioning

confidence: 99%

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

et al. 2019

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“…RPIFSE combines deep CNNs and feature selection methods in an ensemble way to classify RNA–protein pairs (L. Wang et al, ). ELM* first uses deep CNNs to extract high‐level features from sequences, which is further fed into an extreme learning machine (ELM) for classification (L. Wang et al, ). Currently deep learning‐based methods for predicting protein–RNA interaction pairs is still not in an end‐to‐end way, they all first use stacked autoencoder or CNNs to extract high‐level features, which is further fed into a conventional machine learning model.…”

Section: Deep Learning In Rna–protein Interaction Predictionmentioning

confidence: 99%

Recent methodology progress of deep learning for RNA–protein interaction prediction

et al. 2019

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Interactions between RNAs and proteins play essential roles in many important biological processes. Benefitting from the advances of next generation sequencing technologies, hundreds of RNA-binding proteins (RBP) and their associated RNAs have been revealed, which enables the large-scale prediction of RNA-protein interactions using machine learning methods. Till now, a wide range of computational tools and pipelines have been developed, including deep learning models, which have achieved remarkable performance on the identification of RNA-protein binding affinities and sites. In this review, we provide an overview of the successful implementation of various deep learning approaches for predicting RNA-protein interactions, mainly focusing on the prediction of RNA-protein interaction pairs and RBP-binding sites on RNAs. Furthermore, we discuss the advantages and disadvantages of these approaches, and highlight future perspectives on how to design better deep learning models. Finally, we suggest some promising future directions of computational tasks in the study of RNA-protein interactions, especially the interactions between noncoding RNAs and proteins. deep learning, feature representation, machine learning, motif discovery, RNA-protein interactions *The first two authors are co-first authors.

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“…NcRNA-Protein interactions (ncRPIs) play an essential role in many biological functions. Many ncRNAs play a regulatory role in DNA replication, translation, RNA splicing, and gene expression (such as trans-acting and cis-acting), genome defense and so on [5][6][7]. Meanwhile, a variety of diseases can be caused by mutations or imbalances in the composition of ncRNAs in the body, such as cancer [8], Prader-Wills syndrome [9], autism [10], Alzheimer's disease [11], cartilage-hair hypoplasia [12], hearing loss [13].…”

Section: Introductionmentioning

confidence: 99%

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

You

Wang

et al. 2020

BMC Bioinformatics

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Background: The interactions between non-coding RNAs (ncRNA) and proteins play an essential role in many biological processes. Several high-throughput experimental methods have been applied to detect ncRNA-protein interactions. However, these methods are time-consuming and expensive. Accurate and efficient computational methods can assist and accelerate the study of ncRNA-protein interactions.Results: In this work, we develop a stacking ensemble computational framework, RPI-SE, for effectively predicting ncRNA-protein interactions. More specifically, to fully exploit protein and RNA sequence feature, Position Weight Matrix combined with Legendre Moments is applied to obtain protein evolutionary information. Meanwhile, k-mer sparse matrix is employed to extract efficient feature of ncRNA sequences. Finally, an ensemble learning framework integrated different types of base classifier is developed to predict ncRNA-protein interactions using these discriminative features. The accuracy and robustness of RPI-SE was evaluated on three benchmark data sets under five-fold cross-validation and compared with other state-of-the-art methods. Conclusions:The results demonstrate that RPI-SE is competent for ncRNA-protein interactions prediction task with high accuracy and robustness. It's anticipated that this work can provide a computational prediction tool to advance ncRNA-protein interactions related biomedical research.

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Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions

Cited by 54 publications

References 41 publications

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

sefOri: selecting the best-engineered sequence features to predict DNA replication origins

Recent methodology progress of deep learning for RNA–protein interaction prediction

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

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