Abstract
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.
Keywords: Coronavirus, lethality inference, alignment-free, machine learning, genomic nucleotide, SARS-CoV
Current Genomics
Title:Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences
Volume: 22 Issue: 8
Author(s): Rui Yin*, Zihan Luo and Chee Keong Kwoh
Affiliation:
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798,Singapore
Keywords: Coronavirus, lethality inference, alignment-free, machine learning, genomic nucleotide, SARS-CoV
Abstract: 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.
Export Options
About this article
Cite this article as:
Yin Rui*, Luo Zihan and Kwoh Keong Chee, Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic Sequences, Current Genomics 2021; 22 (8) . https://dx.doi.org/10.2174/1389202923666211221110857
DOI https://dx.doi.org/10.2174/1389202923666211221110857 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
Call for Papers in Thematic Issues
Advanced AI Techniques in Big Genomic Data Analysis
The thematic issue on "Advanced AI Techniques in Big Genomic Data Analysis" aims to explore the cutting-edge methodologies and applications of artificial intelligence (AI) in the realm of genomic research, where vast amounts of data pose both challenges and opportunities. This issue will cover a broad spectrum of AI-driven strategies, ...read more
Current Genomics in Cardiovascular Research
Cardiovascular diseases are the main cause of death in the world, in recent years we have had important advances in the interaction between cardiovascular disease and genomics. In this Research Topic, we intend for researchers to present their results with a focus on basic, translational and clinical investigations associated with ...read more
Deep learning in Single Cell Analysis
The field of biology is undergoing a revolution in our ability to study individual cells at the molecular level, and to integrate data from multiple sources and modalities. This has been made possible by advances in technologies for single-cell sequencing, multi-omics profiling, spatial transcriptomics, and high-throughput imaging, as well as ...read more
Genomic Insights into Oncology: Harnessing Machine Learning for Breakthroughs in Cancer Genomics.
This special issue aims to explore the cutting-edge intersection of genomics and oncology, with a strong emphasis on original data and experimental validation. While maintaining the focus on how machine learning and advanced data analysis techniques are revolutionizing our understanding and treatment of cancer, this issue will prioritize contributions that ...read more
Related Journals
![Wayfinder Image](/images/wayfinder.jpg)
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements
Related Articles
-
Review on Deep Learning Methodologies in Medical Image Restoration
and Segmentation
Current Medical Imaging Neurotoxicity of Pesticides: The Roadmap for the Cubic Mode of Action
Current Medicinal Chemistry Central Composite Designed Imatinib-Loaded Magnetic Nanoparticles
Current Nanomedicine Coronavirus: History, Genome Structure and Pathogenesis
Coronaviruses Accumulation Effects of 3-N-Benzyloxycarbonyl-β-aminobutyric Salicylate on the Alkaloids in <i>Catharanthus roseus</i> Cells
Letters in Drug Design & Discovery An Efficient and Environmentally Benign Four-component Domino Protocol for Synthesis of Spirooxindoles Spiroannulated with Fused Heterosystems of Privileged Heterocycles
Current Bioactive Compounds Innovative In Silico Approaches to Address Avian Flu Using Grid Technology
Infectious Disorders - Drug Targets Prevention of Endothelial Cell Injury by Activated Protein C: The Molecular Mechanism(s) and Therapeutic Implications
Current Vascular Pharmacology Recent Advances in the Design and Synthesis of c-Met Inhibitors as Anticancer Agents (2014-Present)
Current Medicinal Chemistry Triazavirin - Potential Inhibitor for 2019-nCoV Coronavirus M Protease: A DFT Study
Current Molecular Medicine Recent Developments of Quinoline Derivatives and their Potential Biological Activities
Current Organic Synthesis Subject Index to Volume 5
Current Protein & Peptide Science In vitro and in silico Activity of Iridoids Against Leishmania amazonensis
Current Drug Discovery Technologies Dissecting the Therapeutic Potency of Antimicrobial Peptides Against Microbial Biofilms
Current Protein & Peptide Science Dexmedetomidine: A Review of its Use for the Management of Pain, Agitation, and Delirium in the Intensive Care Unit
Current Pharmaceutical Design 1,2,4-Oxadiazoles Identified by Virtual Screening and their Non-Covalent Inhibition of the Human 20S Proteasome
Current Medicinal Chemistry Molecular Docking and QSAR Studies of Coumarin Derivatives as NMT Inhibitors: Simple Structural Features as Potential Modulators of Antifungal Activity
Letters in Drug Design & Discovery Coagulopathy and COVID-19
Current Drug Targets Systemic Approach to Parenteral Nutrition in the ICU
Current Drug Safety Synthesis and Antimalarial Activity of New Amino Analogues of Amodiaquine
Medicinal Chemistry