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Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

A diagnostic model for COVID-19 based on proteomics analysis.

Walaa Alkady¹, Khaled ElBahnasy², Walaa Gad²

¹Bioinformatics Program, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Computers in Biology and Medicine

|June 5, 2023

Summary

This summary is machine-generated.

This study introduces a new model for early COVID-19 detection and severity prediction using proteins and metabolites. The model achieved 93% accuracy, identifying key biomarkers related to immune and respiratory systems.

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Area of Science:

Biomarker discovery
Machine learning in healthcare
Infectious disease diagnostics

Background:

Early diagnosis of Coronavirus Disease 2019 (COVID-19) is crucial for timely treatment and improved patient outcomes.
Identifying infected cases and predicting disease severity are key challenges in managing the pandemic.

Purpose of the Study:

To develop and evaluate a prediction model for detecting COVID-19 infection and determining disease severity.
To utilize proteins and metabolites as features for accurate COVID-19 classification.

Main Methods:

Feature selection techniques including Principal Component Analysis (PCA), Information Gain (IG), and Analysis of Variance (ANOVA) were employed.
Three machine learning classifiers (K-Nearest Neighbor, Support Vector Machine, Random Forest) were utilized for prediction.
Model performance was assessed using accuracy, sensitivity, specificity, and precision.

Main Results:

The Random Forest (RF) classifier with ANOVA feature selection achieved 92% accuracy.
The highest accuracy of 93% was obtained using the RF classifier with ten selected features (7 proteins, 3 metabolites).
Selected features were found to be related to the immune and respiratory systems.

Conclusions:

The proposed model demonstrates promising results in predicting COVID-19 infection and severity.
Effective feature selection and machine learning classifiers can significantly enhance diagnostic accuracy.
Identified biomarkers offer insights into COVID-19's impact on physiological systems.