Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Randhawa, Gurjit S.; Soltysiak, Maximillian P.M.; Roz, Hadi El; Souza, Camila P. E. de; Hill, Kathleen A.; Kari, Lila

doi:10.1101/2020.02.03.932350

Cited by 153 publications

(155 citation statements)

References 96 publications

(269 reference statements)

Supporting

Mentioning

151

Contrasting

Unclassified

Order By: Relevance

“…1933 virus-antibody sequences and their clinical patient neutralization response were collected that have been trained using an ML model to predict the antibody response. The work in [92] considers a based alignment-free approach for an ultrafast, scalable, and accurate classification of whole COVID-19 genomes. Decision tree coupled with supervised learning has been also used for genome analysis from a large dataset of over 5000 unique viral genomic sequences.…”

Section: Coronavirus Diagnosis and Treatmentmentioning

confidence: 99%

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Nguyen¹,

Ding²,

Pathirana³

et al. 2020

Preprint

View full text Add to dashboard Cite

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

show abstract

Section: Coronavirus Diagnosis and Treatmentmentioning

confidence: 99%

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Nguyen¹,

Ding²,

Pathirana³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Nevertheless, machine learning has been strongly proposed as a great potential for the fight against COVID-19 [49,50]. Machine learning delivered promising results in several aspects for mitigation and prevention and have been endorsed in the scientific community for, e.g., case identifications [51], classification of novel pathogens [52], modification of SIR-based models [53], diagnosis [54,55], survival prediction [56], and ICU demand prediction [57]. Furthermore, the non-peer reviewed sources suggest numerous potentials of machine learning to fight COVID-19.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér

Felde

Mosavi

et al. 2020

Preprint

View full text Add to dashboard Cite

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptibleinfected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

show abstract

Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér¹,

Felde²,

Mosavi³

et al. 2020

Preprint

View full text Add to dashboard Cite

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

show abstract

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Cited by 153 publications

References 96 publications

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Contact Info

Product

Resources

About