iPromoter-BnCNN: a novel branched CNN based predictor for identifying and classifying sigma promoters

Amin, Ruhul; Rahman, Chowdhury Rafeed; Ahmed, Sajid; Sifat, Md. Habibur Rahman; Liton, Nazmul Khan; Rahman, Md. Moshiur; Shatabda, Swakkhar

doi:10.1101/2019.12.27.884965

Cited by 6 publications

(9 citation statements)

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“…We used broadly applied methodological measures [14], [36], [39]- [41] to comprehensively analyze the efficiency of the promoter's prediction. These include Matthew's correlation coefficient (MCC), accuracy (Acc), sensitivity (Sn), specificity (Sp), and ROC curve.…”

Section: A Evaluation Parametermentioning

confidence: 99%

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

et al. 2021

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The promoter is a regulatory region of the DNA typically located upstream of a gene and plays a key role in regulating gene transcription. Accurate prediction of promoters is crucial for the analysis of gene expression patterns and for the development and understanding of genetic regulatory networks. Genomes of several species have been sequenced, and their gene content has been established to a large extent. Some bioinformatics algorithms have been developed for predicting promoters with high universality for all kinds of plants; however, few studies have been conducted to identify promoters in rice, which might affect the practical applications. Here, we present a rice promoter prediction tool, Cr-Prom. This predictor has been established using a series of sequence-based features and datasets extracted from the PlantProm and RAP-DB databases. We applied a convolutional neural network (CNN)-based strategy to construct a predictor with robust classification performance. To demonstrate our dominance, we ran experiments on a benchmark dataset using 5-fold cross-validation and compared our results with existing techniques using four figure of merits. In addition, CR-Prom was analyzed on an independent dataset. Based on the results, Cr-Prom outperformed the existing rice-specific promoter predictors.

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Section: A Evaluation Parametermentioning

confidence: 99%

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

et al. 2021

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“…The kernel size and filter number of each convolution layer have been shown in the figure. 1D CNN plays important role in position independent local feature extraction [32,33]. Each branch of the architecture learns class distinguishing features regarding its corresponding input feature matrix independent of the other three branches.…”

Section: Model Architecturementioning

confidence: 99%

“…The third branch uses dimer physicochemical property based representation. These properties played an important role in DNA specific classification and identification tasks such as promoter classification [33], 6mA site identification [17], recombination spot identification [34] and DNase I hypersensitive sites identification [35]. This third branch facilitates learning the interaction between these property values in a sequence order basis.…”

Section: Model Architecturementioning

confidence: 99%

A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome

Rahman

Amin

Shatabda

et al. 2020

Preprint

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ABSTRACTDNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification and is responsible for many biological functions. Experimental methods for genome wide 6mA site detection is an expensive and manual labour intensive process. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves area under the receiver operating characteristic curve of 0.98 with an overall accuracy of 0.94 using 5 fold cross validation on benchmark dataset. Finally, we evaluate our model on two other plant genome 6mA site identification datasets besides rice. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. Supplementary data (benchmark dataset, independent test dataset, comparison purpose dataset, trained model, physicochemical property values, attention mechanism details for motif finding) are available at Nucleic Acids Research online. Web tool for this research can be found here.

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“…Although recurrent layers are reliable for converting biological sequences into fixedsize features vectors [16], convolutional layers have also demonstrated promising performance addressing similar problems. In fact, CNN have been demonstrated as an effective technique for feature extraction and classification for DNA, RNA, peptides, and protein sequences in a wide range of studies [29][30][31][32][33][34][35][36]. However, CNN has never been used for ACP classification task.…”

Section: Introductionmentioning

confidence: 99%

ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides

Ahmed

Muhammod

Adilina

et al. 2020

Preprint

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Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification through simultaneous interaction with different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN.

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iPromoter-BnCNN: a novel branched CNN based predictor for identifying and classifying sigma promoters

Cited by 6 publications

References 50 publications

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

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

A Convolution Based Computational Approach Towards DNA N6-methyladenine Site Identification and Motif Extraction in Rice Genome

ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides

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