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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms.

Om Prakash Singh1, Marta Vallejo2, Ismail M El-Badawy3

  • 1School of Chemistry, University of Edinburgh, Edinburgh, UK.

Computers in Biology and Medicine
|July 30, 2021
PubMed
Summary

This study introduces an alignment-free method using digital signal processing and machine learning to classify SARS-CoV-2. The random forest model achieved 97.4% accuracy, efficiently identifying the virus from other coronaviruses.

Keywords:
BiomarkerCOVID-19Machine learningSignal processing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The SARS-CoV-2 pandemic necessitates advanced methods for virus identification and understanding.
  • Digital signal processing (DSP) and machine learning offer powerful tools for analyzing complex biological data.
  • Classifying SARS-CoV-2 accurately is crucial for disease control and mitigation efforts.

Purpose of the Study:

  • To develop and evaluate an alignment-free computational approach for classifying SARS-CoV-2.
  • To identify key genomic biomarkers indicative of SARS-CoV-2.
  • To compare the performance of various machine learning classifiers for SARS-CoV-2 detection.

Main Methods:

  • Collected 1582 SARS-CoV-2 and non-SARS-CoV-2 genome sequences.
  • Extracted eight biomarkers using DSP techniques based on three-base periodicity.
  • Utilized filter-based feature selection to rank biomarkers.
  • Trained and tested k-nearest neighbor, support vector machines, decision trees, and random forest classifiers.
  • Employed 10-fold cross-validation and 10x10 cross-validation paired t-test for performance evaluation.

Main Results:

  • The random forest classifier demonstrated superior performance.
  • Achieved 97.4% accuracy, 96.2% sensitivity, and 98.2% specificity on unseen data.
  • The algorithm was computationally efficient, calculating genome biomarkers in 0.31 seconds.

Conclusions:

  • The proposed alignment-free DSP and machine learning approach effectively classifies SARS-CoV-2.
  • Random forest is a robust model for differentiating SARS-CoV-2 from other coronaviruses.
  • The method is computationally efficient and shows promise for rapid viral identification.