Classifying COVID-19 based on amino acids encoding with machine learning algorithms

Alkady, Walaa; El-Bahnasy, Khaled; Leiva, Víctor; Gad, Walaa

doi:10.1016/j.chemolab.2022.104535

Cited by 20 publications

(16 citation statements)

References 41 publications

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“…The dataset is divided into groups based on entropy by the DT classifier [18]. After determining the entropy values for each sample, the DT splits the dataset using the Information Gain.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Comparative Study on Feature Selection Methods for Protein

Alkady

El-Bahnasy

Gad

2022

IJICIS

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The automated and high-throughput identification of protein function is one of the main issues in computational biology. Predicting the protein's structure is a crucial step in this procedure. In recent years, a wide range of approaches for predicting protein structure has been put forth. They can be divided into two groups: database-based and sequence-based. The first is to identify the principles behind protein structure and attempts to extract valuable characteristics from amino acid sequences. The second one uses pre-existing public annotation databases for data mining. This study emphasizes the sequence-based method and makes use of the ability of amino acid sequences to predict protein activity. The amino acid composition approach, the amino acid tuple approach, and several optimization algorithms were compared. Different protein sequence data sets were used in our experiments. Five classifiers were tested in this research. The best accuracy is 98% using across 10fold cross-validation. This represents the highest performance in the Human dataset.

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“…The dataset is divided into groups based on entropy by the DT classifier [18]. After determining the entropy values for each sample, the DT splits the dataset using the Information Gain.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Comparative Study on Feature Selection Methods for Protein

Alkady

El-Bahnasy

Gad

2022

IJICIS

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“…This means that these effects can occur if there is a corona infection for more than 12 weeks [5] and it can affect many organs. It can also develop a variety of long-term symptoms.…”

Section: Introductionmentioning

confidence: 99%

Examination of the Effects of Long-term COVID-19 Impacts on Patients with Neurological Disabilities Using a Neuromachine Learning Model

Vaniprabha¹,

Logeshwaran²,

Kiruthiga³

et al. 2023

BIJNN

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Currently, studies have shown that one in three people infected with coronavirus disease-19 (COVID-19) is likely to have had long-term exposure to COVID-19, known as long-term COVID-19. Clinical studies indicate that many people infected with the severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) COVID-19 pandemic have long-term COVID-19 exposure. According to the study, it has been said that people with diabetes and obesity, and people who have received organ transplants, are more likely to suffer from this long-term effect of COVID-19. In this article, the effects of long-term COVID-19 exposure on neurological disability patients are analyzed with the help of a neuromachine learning model. The proposed model also shows that this long-term COVID problem does not depend on the factors such as race, age, gender, and socioeconomic status of those people. According to the proposed model, people suffering from long-term COVID problems continue to suffer from physical fatigue and shortness of breath and are regularly monitored and classified as per the proposed instructions. Even after they recover from the disease, various side effects are seen.

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“…Different types of SARS-CoV-2 have been identified (Alkadya et al. 2022 ). COVID-19 has revolutionized the world’s population, as well as the economy (Chahuan-Jimenez et al.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries

Sardar

Akbar

Leiva

et al. 2022

Stoch Environ Res Risk Assess

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Machine learning (ML) has proved to be a prominent study field while solving complex real-world problems. The whole globe has suffered and continues suffering from Coronavirus disease 2019 (COVID-19), and its projections need to be forecasted. In this article, we propose and derive an autoregressive modeling framework based on ML and statistical methods to predict confirmed cases of COVID-19 in the South Asian Association for Regional Cooperation (SAARC) countries. Automatic forecasting models based on autoregressive integrated moving average (ARIMA) and Prophet time series structures, as well as extreme gradient boosting, generalized linear model elastic net (GLMNet), and random forest ML techniques, are introduced and applied to COVID-19 data from the SAARC countries. Different forecasting models are compared by means of selection criteria. By using evaluation metrics, the best and suitable models are selected. Results prove that the ARIMA model is found to be suitable and ideal for forecasting confirmed infected cases of COVID-19 in these countries. For the confirmed cases in Afghanistan, Bangladesh, India, Maldives, and Sri Lanka, the ARIMA model is superior to the other models. In Bhutan, the Prophet time series model is appropriate for predicting such cases. The GLMNet model is more accurate than other time-series models for Nepal and Pakistan. The random forest model is excluded from forecasting because of its poor fit.

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Classifying COVID-19 based on amino acids encoding with machine learning algorithms

Cited by 20 publications

References 41 publications

Comparative Study on Feature Selection Methods for Protein

Comparative Study on Feature Selection Methods for Protein

Examination of the Effects of Long-term COVID-19 Impacts on Patients with Neurological Disabilities Using a Neuromachine Learning Model

Machine learning and automatic ARIMA/Prophet models-based forecasting of COVID-19: methodology, evaluation, and case study in SAARC countries

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