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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Related Experiment Video

Updated: Sep 29, 2025

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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Classifying COVID-19 based on amino acids encoding with machine learning algorithms.

Walaa Alkady1, Khaled ElBahnasy2, Víctor Leiva3

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

Chemometrics and Intelligent Laboratory Systems : an International Journal Sponsored by the Chemometrics Society
|March 21, 2022
PubMed
Summary
This summary is machine-generated.

A novel amino acid encoding based prediction (AAPred) model accurately classifies coronaviruses, distinguishing SARS-CoV-2. This machine learning approach identifies key features for understanding viral infection cycles and vaccine development.

Keywords:
ANOVAAmino acid compositionArtificial intelligenceBagging ensemble and gradient boostingChi-square testDeep learningFeature extraction and selectionInformation gainLASSOMolecular modelingProtein sequenceSARS-CoV-2

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Area of Science:

  • Computational biology
  • Virology
  • Machine learning applications in bioinformatics

Background:

  • COVID-19 poses significant respiratory health risks, necessitating a deep understanding of its viral infection cycle.
  • Identifying viral proteins that interact with human receptors is crucial for developing effective vaccines.
  • Accurate classification of coronaviruses, particularly distinguishing SARS-CoV-2, is vital for targeted therapeutic strategies.

Purpose of the Study:

  • To introduce a novel computational model, amino acid encoding based prediction (AAPred), for COVID-19 analysis.
  • To enhance model performance by reducing features using statistical criteria and identifying key protein sequence characteristics.
  • To classify coronavirus types and differentiate SARS-CoV-2 from other strains.

Main Methods:

  • Development of the AAPred model utilizing amino acid encoding for protein sequence analysis.
  • Feature selection employing information gain to identify the most significant features.
  • Evaluation of the model using six machine learning classifiers: decision trees, k-nearest neighbors, random forest, support vector machine, bagging ensemble, and gradient boosting.
  • Computational implementation and validation using real-world data from the National Genomics Data Center.

Main Results:

  • The AAPred model successfully reduced the number of predictive features to seven.
  • Achieved high performance metrics: 98.69% average accuracy, 98.72% precision, 96.81% sensitivity, and 97.72% specificity via 10-fold cross-validation.
  • Identified similarities in physicochemical characteristics and infection cycles between SARS-CoV-2 and SARS-CoV in specific regions.
  • Random forest classifier, combined with information gain for feature selection, proved most effective.

Conclusions:

  • The AAPred model offers an accurate and efficient method for classifying coronaviruses and distinguishing SARS-CoV-2.
  • The identified similarities between SARS-CoV-2 and SARS-CoV suggest potential cross-effectiveness of certain vaccines.
  • The study highlights the utility of machine learning in analyzing viral protein sequences for understanding infection mechanisms and guiding vaccine development.