Identification of Pathogenic Viruses Using Genomic Cepstral Coefficients with Radial Basis Function Neural Network

Adetiba, Emmanuel; Olugbara, Oludayo O.; Taiwo, Tunmike B.

doi:10.1007/978-3-319-27400-3_25

Cited by 17 publications

(6 citation statements)

References 15 publications

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“…This is mandatory, as the existing clustering approaches can only deal with numerical vectors but not string vectors. Several approaches are available to convert SNP literals into numerical vectors [ 46 , 47 , 48 ].…”

Section: Methodsmentioning

confidence: 99%

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Agapito

Milano

Cannataro

2022

Genes

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Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.

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Section: Methodsmentioning

confidence: 99%

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Agapito

Milano

Cannataro

2022

Genes

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“…This is important as the existing machine learning approaches can only deal with vectors but not sequence samples. Several methods are proposed that convert genomic sequences into numerical vectors, e.g., the fixed mapping between nucleotides and real numbers without biological significance [ 35 ], based on physio-chemical properties [ 36 ], deduction from doublets or codons [ 37 ], and chaos game representation [ 38 ]. To accommodate comprehensive analysis and comparison, we adapt different types of numerical representations for biological RNA sequences.…”

Section: Methodsmentioning

confidence: 99%

Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences

Yin

Luo

Kwoh

2021

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Background: A newly emerging novel coronavirus appeared and rapidly spread worldwide and World Health Organization declared a pandemic on March 11, 2020. The roles and characteristics of coronavirus have captured much attention due to its power of causing a wide variety of infectious diseases, from mild to severe, on humans. The detection of the lethality of human coronavirus is key to estimate the viral toxicity and provide perspectives for treatment. Methods: We developed an alignment-free framework that utilizes machine learning approaches for an ultra-fast and highly accurate prediction of the lethality of human-adapted coronavirus using genomic sequences. We performed extensive experiments through six different feature transformation and machine learning algorithms combining digital signal processing to identify the lethality of possible future novel coronaviruses using existing strains. Results: The results tested on SARS-CoV, MERS-CoV and SARS-CoV-2 datasets show an average 96.7% prediction accuracy. We also provide preliminary analysis validating the effectiveness of our models through other human coronaviruses. Our framework achieves high levels of prediction performance that is alignment-free and based on RNA sequences alone without genome annotations and specialized biological knowledge. Conclusion: The results demonstrate that, for any novel human coronavirus strains, this study can offer a reliable real-time estimation for its viral lethality.

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“…A multitude of alignment-free sequence comparison algorithms have been developed in recent years, such as conditional Lempel-Ziv [4] and Kolmogorov complexity [5], measure representation [6], Markov model comparisons and frequent substring lengths [7,8], which divides the genome into regions that represent a system that is evolving over time with hidden states. Base-base correlation [9], spectral distortion [10], primitive discrimination substrings [11], Burrows-Wheeler similarity [12], normalized central moments, nearest-neighbor interactions [13], subword composition [14], prefix codes [15], information correlation [16], the context-object model [17], and spaced word frequencies [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach

Hossain,

Kim,

Uddin

et al. 2023

Bioengineering

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The application of deep learning for taxonomic categorization of DNA sequences is investigated in this study. Two deep learning architectures, namely the Stacked Convolutional Autoencoder (SCAE) with Multilabel Extreme Learning Machine (MLELM) and the Variational Convolutional Autoencoder (VCAE) with MLELM, have been proposed. These designs provide precise feature maps for individual and inter-label interactions within DNA sequences, capturing their spatial and temporal properties. The collected features are subsequently fed into MLELM networks, which yield soft classification scores and hard labels. The proposed algorithms underwent thorough training and testing on unsupervised data, whereby one or more labels were concurrently taken into account. The introduction of the clade label resulted in improved accuracy for both models compared to the class or genus labels, probably owing to the occurrence of large clusters of similar nucleotides inside a DNA strand. In all circumstances, the VCAE-MLELM model consistently outperformed the SCAE-MLELM model. The best accuracy attained by the VCAE-MLELM model when the clade and family labels were combined was 94%. However, accuracy ratings for single-label categorization using either approach were less than 65%. The approach’s effectiveness is based on MLELM networks, which record connected patterns across classes for accurate label categorization. This study advances deep learning in biological taxonomy by emphasizing the significance of combining numerous labels for increased classification accuracy.

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Identification of Pathogenic Viruses Using Genomic Cepstral Coefficients with Radial Basis Function Neural Network

Cited by 17 publications

References 15 publications

A Python Clustering Analysis Protocol of Genes Expression Data Sets

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences

Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach

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