Deep Learning for the Classification of Genomic Signals

Morales, J. Alejandro; Saldaña, Román; Santana-Castolo, Manuel H.; Torres-Cerna, Carlos E.; Borrayo, Ernesto; Mendizabal-Ruiz, Adriana P.; Vélez-Pérez, Hugo; Mendizabal-Ruiz, Gerardo

doi:10.1155/2020/7698590

Cited by 18 publications

(21 citation statements)

References 43 publications

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“…Simple CNN, ResNet, WaveNet, and Inception are among the best CNNs networks widely used in biomedical signals analysis studies. Based on recent works [ 42 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ], a comparative analysis is provided in the following using various performance criteria as complexity , 1D-dimension , performance and time-consumption . In this regard, specific three tests (2, 3 and 4 states) with various values are given for each criterion as following.…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…They used deep learning to classify genetic markers for liver cancer from the hepatitis B virus DNA sequence in this analysis, and the training dataset had an accuracy of about 96.83% [ 22 ]. The DL architecture is proposed to classify three genome types of the coding region, long noncoding region, and pseudoregions and achieve an average accuracy of 84% [ 23 ]. The author used the one-hot encoding technique to represent the sequences of DNA.…”

Section: Introductionmentioning

confidence: 99%

Analysis of DNA Sequence Classification Using CNN and Hybrid Models

Gunasekaran

Ramalakshmi

Arokiaraj

et al. 2021

Computational and Mathematical Methods in Medicine

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In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K -mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K -mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.

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“…The mapping stage receives the DNA sequence information, that can be the reads, contigs, or the whole genome sequence, and maps this data into a feature space. Various mapping strategies have been present in the works from the state of the art, such as one-hot encoding [13,[16][17][18], number representation [11,12], digital signal processing [19], and other strategies, including multiple mapping strategies applied sequentially [20,21]. The processing stage consists of the utilization of a DNN to perform classification, prediction, and other assumptions about the genome information.…”

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confidence: 99%

“…In the works from [19], [24], [25] and [26] were proposed methodologies to predict or classify specific regions in the genome sequence. [19] presented a methodology for the classification of three different functional genome types: coding regions, long noncoding regions, and pseudogenes in genomic data. They used a digital signal processing (DSP) methods, called Genomic signal processing (GSP), that converts the nucleotide sequence into a graphical representation of the information contained in the sequence.…”

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confidence: 99%

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Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

Coutinho

Câmara

Barbosa

et al. 2021

Preprint

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Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on the stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k=6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

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Deep Learning for the Classification of Genomic Signals

Cited by 18 publications

References 43 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Analysis of DNA Sequence Classification Using CNN and Hybrid Models

Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

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