i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning

Li, Yanjuan; Zhao, Zhengnan; Teng, Zhaogang

doi:10.1155/2021/5515342

Cited by 4 publications

(3 citation statements)

References 58 publications

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“…In TNC, all samples of 41 nt produce 39 components with the equation of L − k + 1. Here, L stands for the sequence length, and k stands for the K-mer value as an integer [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. ATG, TGC, GCG, and CGA are the four 3-mers that can be tokenized from the DNA sequence “ATGCGA,” for instance.…”

Section: Methodsmentioning

confidence: 99%

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4—Methylcytosine Using Deep Learning Approach

Halder

Uddin

et al. 2023

Genes

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DNA (Deoxyribonucleic Acid) N4-methylcytosine (4mC), a kind of epigenetic modification of DNA, is important for modifying gene functions, such as protein interactions, conformation, and stability in DNA, as well as for the control of gene expression throughout cell development and genomic imprinting. This simply plays a crucial role in the restriction–modification system. To further understand the function and regulation mechanism of 4mC, it is essential to precisely locate the 4mC site and detect its chromosomal distribution. This research aims to design an efficient and high-throughput discriminative intelligent computational system using the natural language processing method “word2vec” and a multi-configured 1D convolution neural network (1D CNN) to predict 4mC sites. In this article, we propose a grid search-based multi-layer dynamic ensemble system (GS-MLDS) that can enhance existing knowledge of each level. Each layer uses a grid search-based weight searching approach to find the optimal accuracy while minimizing computation time and additional layers. We have used eight publicly available benchmark datasets collected from different sources to test the proposed model’s efficiency. Accuracy results in test operations were obtained as follows: 0.978, 0.954, 0.944, 0.961, 0.950, 0.973, 0.948, 0.952, 0.961, and 0.980. The proposed model has also been compared to 16 distinct models, indicating that it can accurately predict 4mC.

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Section: Methodsmentioning

confidence: 99%

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4—Methylcytosine Using Deep Learning Approach

Halder

Uddin

et al. 2023

Genes

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“…These methods are simple to use, but their capacity of detecting 4mC sites cross species need promoting. No less than five computational methods [ 36 – 41 ] were intended to predict 4mC sites in mouse genomes. Both 4mCPred-EL [ 36 ] and i4mC-Mouse [ 37 ] were feature engineering-based methods.…”

Section: Introductionmentioning

confidence: 99%

MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction

et al. 2023

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N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitation of techniques, precise detection of 4mC is still a challenging task. In this paper, we presented a multi-scale convolution neural network (CNN) and adaptive embedding-based computational method for predicting 4mC sites in mouse genome, which was referred to as MultiScale-CNN-4mCPred. The MultiScale-CNN-4mCPred used adaptive embedding to encode nucleotides, and then utilized multi-scale CNNs as well as long short-term memory to extract more in-depth local properties and contextual semantics in the sequences. The MultiScale-CNN-4mCPred is an end-to-end learning method, which requires no sophisticated feature design. The MultiScale-CNN-4mCPred reached an accuracy of 81.66% in the 10-fold cross-validation, and an accuracy of 84.69% in the independent test, outperforming state-of-the-art methods. We implemented the proposed method into a user-friendly web application which is freely available at: http://www.biolscience.cn/MultiScale-CNN-4mCPred/.

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“…These predictors typically make use of machine learning algorithms to learn from available data to perform novel predictions and gain new insights. Recently, a variety of machine learning algorithms are useful for this goal, such as support vector machine (SVM) ( Chen et al, 2017 ; He et al, 2019 ; Wei et al, 2019a , b ; Lv et al, 2020b ; Zhao et al, 2020 ), random forest (RF) ( Hasan et al, 2020a , b ; Lv et al, 2020a ; Alghamdi et al, 2021 ; Zulfiqar et al, 2021a ), Markov model (MM) ( Yang et al, 2020 ), and the combined or ensemble methods ( Gong and Fan, 2019 ; Manavalan et al, 2019a , b ; Tang et al, 2020 ; Li et al, 2021 ), extreme gradient boosting (XGBoost) ( Wang et al, 2021 ) and Laplacian Regularized Sparse Representation ( Ding et al, 2021 ). As shown in Supplementary Table 1 , SVM is the most widely used traditional machine learning algorithms in the model development and method comparison for 4mC prediction, followed by RF.…”

Section: Introductionmentioning

confidence: 99%

Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning

Zhang

et al. 2022

Front. Microbiol.

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DNA N4-methylcytosine (4mC) is a pivotal epigenetic modification that plays an essential role in DNA replication, repair, expression and differentiation. To gain insight into the biological functions of 4mC, it is critical to identify their modification sites in the genomics. Recently, deep learning has become increasingly popular in recent years and frequently employed for the 4mC site identification. However, a systematic analysis of how to build predictive models using deep learning techniques is still lacking. In this work, we first summarized all existing deep learning-based predictors and systematically analyzed their models, features and datasets, etc. Then, using a typical standard dataset with three species (A. thaliana, C. elegans, and D. melanogaster), we assessed the contribution of different model architectures, encoding methods and the attention mechanism in establishing a deep learning-based model for the 4mC site prediction. After a series of optimizations, convolutional-recurrent neural network architecture using the one-hot encoding and attention mechanism achieved the best overall prediction performance. Extensive comparison experiments were conducted based on the same dataset. This work will be helpful for researchers who would like to build the 4mC prediction models using deep learning in the future.

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i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning

Cited by 4 publications

References 58 publications

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4—Methylcytosine Using Deep Learning Approach

A Grid Search-Based Multilayer Dynamic Ensemble System to Identify DNA N4—Methylcytosine Using Deep Learning Approach

MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction

Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning

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