XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials

Alam, Waleed; Tayara, Hilal; Chong, Kil To

doi:10.1038/s41598-020-77824-2

Cited by 45 publications

(33 citation statements)

References 38 publications

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“…To the best of our knowledge, only two computation methods, PACES ( Zhao et al , 2019 ) and XG-ac4C ( Alam et al , 2020 ), have been developed so far for the prediction of ac 4 C from sequences, and again, both were based on strong supervision. Although both approaches achieved positive prediction performance, they require a very specific sequence pattern and consider only sequences that have at least five continuous CXX repeats, which may limit the applicable scope of these methods.…”

Section: Resultsmentioning

confidence: 99%

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Huang

Wei

et al. 2021

Bioinformatics

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Motivation Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available. Results We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution. Availability implementation The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Huang

Wei

et al. 2021

Bioinformatics

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“…Electron–Ion Interaction Pseudopotentials (EIIP) represent the energy of delocalized electrons in nucleotides. They have been used as a composition measure that has been effective in several bioinformatics algorithms [34] , [35] . Using the EIIP technique, every nucleotide in an RNA sequence is encoded using the distribution of free electron energies.…”

Section: Feature Extractionmentioning

confidence: 99%

Machine learning applications in RNA modification sites prediction

Allali¹,

Elhamraoui²,

Daoud³

2021

Computational and Structural Biotechnology Journal

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“…The convolutional layer is used to automatically extract important features from an encoded DNA sequence. We apply the nucleotide chemical properties (NCP) and nucleotide density (ND) methods to encode the input DNA sequences [25,31,32]. Moreover, we use the batch normalization and dropout layers to control overfitting.…”

Section: Introductionmentioning

confidence: 99%

i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties

Alam

Tayara

Chong

2021

Genes

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DNA is subject to epigenetic modification by the molecule N4-methylcytosine (4mC). N4-methylcytosine plays a crucial role in DNA repair and replication, protects host DNA from degradation, and regulates DNA expression. However, though current experimental techniques can identify 4mC sites, such techniques are expensive and laborious. Therefore, computational tools that can predict 4mC sites would be very useful for understanding the biological mechanism of this vital type of DNA modification. Conventional machine-learning-based methods rely on hand-crafted features, but the new method saves time and computational cost by making use of learned features instead. In this study, we propose i4mC-Deep, an intelligent predictor based on a convolutional neural network (CNN) that predicts 4mC modification sites in DNA samples. The CNN is capable of automatically extracting important features from input samples during training. Nucleotide chemical properties and nucleotide density, which together represent a DNA sequence, act as CNN input data. The outcome of the proposed method outperforms several state-of-the-art predictors. When i4mC-Deep was used to analyze G. subterruneus DNA, the accuracy of the results was improved by 3.9% and MCC increased by 10.5% compared to a conventional predictor.

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XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials

Cited by 45 publications

References 38 publications

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Machine learning applications in RNA modification sites prediction

i4mC-Deep: An Intelligent Predictor of N4-Methylcytosine Sites Using a Deep Learning Approach with Chemical Properties

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