DNA Sequences Classification with Deep Learning: A Survey

–Alhalem, Samia M. Abd; El-Rabaie, El-Sayed M.; Soliman, Naglaa F.; Abdulrahman, Salah Eldin S. E.; Ismail, Nabil A.; El‐Samie, Fathi E. Abd

doi:10.21608/mjeer.2021.146090

Cited by 10 publications

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the sheer volume and complexity of this data presents significant analytical challenges, necessitating the development of sophisticated computational tools. In response to these challenges, machine learning and deep learning approaches have gained prominence [5,6,7]. These methodologies are known for their ability to handle large datasets and extract meaningful patterns.…”

Section: Introductionmentioning

confidence: 99%

Integration of Deep Learning Models for Enhanced Classification of Viral DNA Sequences Across Specific Viruses and Viral Families

El-Tohamy,

Amin,

Badr

2024

IJICIS

View full text Add to dashboard Cite

The field of genomic bioinformatics is continually challenged by the need for precise classification of viral DNA sequences. The challenge of accurately classifying viral sequences is crucial for the development of diagnostic and therapeutic strategies for any viral outbreaks. This study presents a comprehensive approach integrating two distinct deep learning models, namely the Genetic Algorithm (GA) optimized Convolutional Neural Networks (CNN) hybrid model and the CNN-Extreme Learning Machines (ELM) model aiming to enhance the classification of viral DNA sequences across specific viruses and viral families.A comprehensive data preprocessing strategy is employed, wherein both datasets undergo k-mer, label, and one-hot vector encoding. This allows for a uniform and comparative analysis across different models and datasets. When the optimized GA-CNN is applied to the more generic viral family dataset, it demonstrates a good adaptability with an accuracy of 95.88% achieving a higher result than the CNN-ELM. In contrast, the CNN-ELM, when tested on the specific virus dataset, maintains robust feature extraction capabilities, faster training time but lower than the optimized GA-CNN model achieving an accuracy of 92.7%. A comparative analysis of training times is also employed in this study. The CNN-ELM model shows a notable efficiency, with a 34% faster training time compared to the GA-CNN. Moreover, when both models are applied to the new generic dataset, a comparative study with other deep learning models is conducted. Remarkably, the GA-CNN outperforms other models, achieving the highest classification accuracy of 95.88%.

show abstract

Section: Introductionmentioning

confidence: 99%

Integration of Deep Learning Models for Enhanced Classification of Viral DNA Sequences Across Specific Viruses and Viral Families

El-Tohamy,

Amin,

Badr

2024

IJICIS

View full text Add to dashboard Cite

show abstract

“…These models can deal with complex challenges because of their many hidden layers. Many studies have used machine learning and deep learning algorithms to analyze DNA sequences [6,7]. Manual feature extraction is used in these machine learning models [8].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

El-Tohamy¹,

Maghwary²,

Badr³

2022

IJACSA

View full text Add to dashboard Cite

DNA sequence classification is one of the major challenges in biological data processing. The identification and classification of novel viral genome sequences drastically help in reducing the dangers of a viral outbreak like COVID-19. The more accurate the classification of these viruses, the faster a vaccine can be produced to counter them. Thus, more accurate methods should be utilized to classify the viral DNA. This research proposes a hybrid deep learning model for efficient viral DNA sequence classification. A genetic algorithm (GA) was utilized for weight optimization with Convolutional Neural Networks (CNN) architecture. Furthermore, Long Short-Term Memory (LSTM) as well as Bidirectional CNN-LSTM model architectures are employed. Encoding methods are needed to transform the DNA into numeric format for the proposed model. Three different encoding methods to represent DNA sequences as input to the proposed model were experimented: k-mer, label encoding, and one hot vector encoding. Furthermore, an efficient oversampling method was applied to overcome the imbalanced dataset issues. The performance of the proposed GA optimized CNN hybrid model using label encoding achieved the highest classification accuracy of 94.88% compared with other encoding methods.

show abstract

Accelerating the Performance of Sequence Classification Using GPU Based Ensemble Learning with Extreme Gradient Boosting

Kaur

Sagar²,

Chakraborty³

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

DNA Sequences Classification with Deep Learning: A Survey

Cited by 10 publications

References 44 publications

Integration of Deep Learning Models for Enhanced Classification of Viral DNA Sequences Across Specific Viruses and Viral Families

Integration of Deep Learning Models for Enhanced Classification of Viral DNA Sequences Across Specific Viruses and Viral Families

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

Accelerating the Performance of Sequence Classification Using GPU Based Ensemble Learning with Extreme Gradient Boosting

Contact Info

Product

Resources

About