Identification of Functional piRNAs Using a Convolutional Neural Network

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

doi:10.1109/tcbb.2020.3034313

Cited by 27 publications

(20 citation statements)

References 49 publications

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“…These models have accomplished outstanding results in different fields, generally because of the ongoing improvement of convolutional neural networks. CNNs achieved record-breaking results in medical image processing [ 29 , 30 ] and in computational biology [ 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Moreover, there are several remarkable examples of the use of CNNs to produce a prediction system that can identify the effects of genetic variation.…”

Section: Proposed Methodologymentioning

confidence: 99%

pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters

Shujaat

Wahab

Tayara

et al. 2020

Genes

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A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools.

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Section: Proposed Methodologymentioning

confidence: 99%

pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters

Shujaat

Wahab

Tayara

et al. 2020

Genes

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“…Various performance assessment measures are utilized to examine the success rates of learning algorithms [33], [48], [69]- [72]. Here, Accuracy (Acc), sensitivity (Sn), specificity (Sp) and Mathew's correlation coefficient (MCC) are employed.…”

Section: Prediction Quality Measurementmentioning

confidence: 99%

Prediction of Piwi-Interacting RNAs and Their Functions via Convolutional Neural Network

et al. 2021

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In eukaryotic cells, Piwi-interacting RNAs (piRNAs) are the type of short chain noncoding RNA molecules, which interconnect with PIWI proteins. It performs various cellular and genetic functions such as gene-specific protein translation, expression regulation, maintenance, and formulation of germ cells. Seeing the prominent contribution of piRNA in eukaryotic organism cells, many attempts were made to identify it computationally, however, unsatisfactory results were obtained. So, it is requisite to extend the concept of a computational tool in such a way that accurately represents piRNA. In this regard, intelligent and high discriminative deep learning i.e., the convolutional neural network based sequentialcomputational model known as "piRNA-CNN" is carried out for the prediction of piRNA. RNA sequences are mathematically expressed using the natural language processing method namely: word2vec in order to get prominent, relevant, and high variated numerical descriptors. The proposed "piRNA-CNN" model yields an accuracy of 93.83% for the first-layer in which the provided query RNA molecule is predicted as non-piRNA or piRNA. In case of the piRNA, the proposed model identified the query as mRNA deadenylation or without deadenylation in the second layer, and achieved 91.19% of accuracy. The obtained outcomes authenticated that the piRNA-CNN model exposed substantial results matched to the current tools stated in the literature, so far. It is further expected that the suggested predictive tool will assist scientists and researchers to design improved computational tools.

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“…By contrast, a computational architecture based on deep learning is capable of extracting the essential features of a sequence without any human intervention, leading to an accurate and robust computational model. Deep learning based models are associated with extraordinary advancements in the fields of natural language processing [44], speech recognition [45], energy load forecasting [46], image recognition [47] and computational biology [48]- [51]. However Recently, advanced machine learning techniques based on deep forest models were proposed such as DTI-CDF [52] and LMI-DForest [53].…”

Section: Introductionmentioning

confidence: 99%