Cr-Prom: A Convolutional Neural Network-Based Model for the Prediction of Rice Promoters

Shujaat, Muhammad; Lee, Seung Beop; Tayara, Hilal; Chong, Kil To

doi:10.1109/access.2021.3086102

Cited by 19 publications

(7 citation statements)

References 41 publications

(36 reference statements)

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“…One-hot-encoding is a simple and efficient way to transform nucleotides into a machine-readable format. It is one of the widely used encoding schemes in the field of bioinformatics [ 30 , 31 , 32 ]. Using this encoding method, the four nucleotides, adenine (A), thymine (T), cytosine (C), and guanine (G), can be represented as A: 1,0,0,0; T: 0,1,0,0 C: 0,0,1,0, and G: 0,0,0,1, respectively.…”

Section: Methodsmentioning

confidence: 99%

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

Abbas

Tayara

Chong

2022

IJMS

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N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.

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Section: Methodsmentioning

confidence: 99%

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

Abbas

Tayara

Chong

2022

IJMS

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“…Methods based on deep‐learning primarily focus on training a neural network with DNA sequences or DNA sequences with epigenomic characteristics (such as histone modifications, chromatin accessibility, DNA methylation, or CpG islands) as inputs. Though some scholars have trained their networks with epigenome features [67,68,71,74,75,82], most have done so with only DNA sequences as inputs [69,70,72,73,77–81,85,88–90,98–100,102,104–111,142]. Predicting enhancers and promoters directly from DNA sequences is believed to be more applicable than identifying them from multiple epigenomic features because the epigenomic characteristics data carries with it substantial sequencing costs, and a high rate of false positives.…”

Section: Prediction Of Enhancer and Promotermentioning

confidence: 99%

Computational methods for identifying enhancer‐promoter interactions

Gong,

Chen,

Tang

et al. 2023

Quant. Biol.

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BackgroundAs parts of the cis‐regulatory mechanism of the human genome, interactions between distal enhancers and proximal promoters play a crucial role. Enhancers, promoters, and enhancer‐promoter interactions (EPIs) can be detected using many sequencing technologies and computation models. However, a systematic review that summarizes these EPI identification methods and that can help researchers apply and optimize them is still needed.ResultsIn this review, we first emphasize the role of EPIs in regulating gene expression and describe a generic framework for predicting enhancer‐promoter interaction. Next, we review prediction methods for enhancers, promoters, loops, and enhancer‐promoter interactions using different data features that have emerged since 2010, and we summarize the websites available for obtaining enhancers, promoters, and enhancer‐promoter interaction datasets. Finally, we review the application of the methods for identifying EPIs in diseases such as cancer.ConclusionsThe advance of computer technology has allowed traditional machine learning, and deep learning methods to be used to predict enhancer, promoter, and EPIs from genetic, genomic, and epigenomic features. In the past decade, models based on deep learning, especially transfer learning, have been proposed for directly predicting enhancer‐promoter interactions from DNA sequences, and these models can reduce the parameter training time required of bioinformatics researchers. We believe this review can provide detailed research frameworks for researchers who are beginning to study enhancers, promoters, and their interactions.

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“…One-hot encoding techniques are used by many state-ofthe-art bioinformatics tools (Umarov and Solovyev, 2017;Liu and Li, 2019;Shujaat et al, 2021;Kim et al, 2022). Each nucleotide in a DNA sequence is represented by a four-dimensional vector, which is a vector of zeros with a single one.…”

Section: One-hot Feature Encodingmentioning

confidence: 99%

iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network

et al. 2022

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The increased interest in phages as antibacterial agents has resulted in a rise in the number of sequenced phage genomes, necessitating the development of user-friendly bioinformatics tools for genome annotation. A promoter is a DNA sequence that is used in the annotation of phage genomes. In this study we proposed a two layer model called “iProm-phage” for the prediction and classification of phage promoters. Model first layer identify query sequence as promoter or non-promoter and if the query sequence is predicted as promoter then model second layer classify it as phage or host promoter. Furthermore, rather than using non-coding regions of the genome as a negative set, we created a more challenging negative dataset using promoter sequences. The presented approach improves discrimination while decreasing the frequency of erroneous positive predictions. For feature selection, we investigated 10 distinct feature encoding approaches and utilized them with several machine-learning algorithms and a 1-D convolutional neural network model. We discovered that the one-hot encoding approach and the CNN model outperformed based on performance metrics. Based on the results of the 5-fold cross validation, the proposed predictor has a high potential. Furthermore, to make it easier for other experimental scientists to obtain the results they require, we set up a freely accessible and user-friendly web server at http://nsclbio.jbnu.ac.kr/tools/iProm-phage/.

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Cr-Prom: A Convolutional Neural Network-Based Model for the Prediction of Rice Promoters

Cited by 19 publications

References 41 publications

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

Computational methods for identifying enhancer‐promoter interactions

iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network

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