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SYSTEMS AND METHODS FOR IDENTIFICATION OF ION CHANNELS

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  • Publication Date:
    September 5, 2024
  • Additional Information
    • Document Number:
      20240296910
    • Appl. No:
      18/442501
    • Application Filed:
      February 15, 2024
    • Abstract:
      Systems and methods for identification of ion channels are described herein. In some implementations, the techniques described herein relate to a computer-implemented method including: receiving a single channel activity signal associated with an ion channel of a cell; performing a time-domain analysis on the single channel activity signal; and identifying, based on the time-domain analysis, an isoform of the ion channel.
    • Claim:
      1. A computer-implemented method comprising: receiving a single channel activity signal associated with an ion channel of a cell; performing a time-domain analysis on the single channel activity signal; and identifying, based on the time-domain analysis, an isoform of the ion channel.
    • Claim:
      2. The computer-implemented method of claim 1, wherein the time-domain analysis comprises dynamic time warping (DTW).
    • Claim:
      3. The computer-implemented method of claim 2, wherein the time-domain analysis comprises calculating a respective Euclidean distance for one or more fluctuations of an amino-acid sequence using DTW.
    • Claim:
      4. The computer-implemented method of claim 1, wherein the isoform of the ion channel is one of a sodium channel (Nav), a potassium channel (Kv), a calcium channel (Cav), or a chloride channel (ClC).
    • Claim:
      5. The computer-implemented method of claim 1, wherein the single channel activity signal is measured using a cell-attached patch-clamp system or an ion conductance microscopy-guided smart patch-clamp system.
    • Claim:
      6. The computer-implemented method of claim 1, wherein performing the time-domain analysis on the single channel activity signal further comprises extracting at least one time-domain feature, the computer-implemented method further comprising: inputting, into a trained machine learning model, the at least one time-domain feature; and predicting, using the trained machine learning model, the isoform of the ion channel.
    • Claim:
      7. The computer-implemented method of claim 6, further comprising comparing the isoform of the ion channel identified based on the time-domain analysis to the isoform of the ion channel predicted by the trained machine learning model.
    • Claim:
      8. The computer-implemented method of claim 6, wherein the at least one time-domain feature comprises a Euclidean distance for one or more fluctuations of an amino-acid sequence.
    • Claim:
      9. The computer-implemented method of claim 6, wherein the trained machine learning model is a supervised learning model.
    • Claim:
      10. The computer-implemented method of claim 9, wherein the supervised learning model is a decision tree classifier, a support vector machine (SVM), a k-nearest neighbors (KNN) classifier, a Naïve Bayes' classifier, or an artificial neural network.
    • Claim:
      11. A computer-implemented method comprising: receiving a single channel activity signal associated with an ion channel of a cell; performing a frequency-domain analysis on the single channel activity signal; and identifying, based on the frequency-domain analysis, an isoform of the ion channel.
    • Claim:
      12. The computer-implemented method of claim 11, wherein the frequency-domain analysis comprises fast Fourier transform (FFT) or discrete Fourier transform (DFT).
    • Claim:
      13. The computer-implemented method of claim 12, wherein the frequency-domain analysis comprises determining a power spectrum of the single channel activity signal using FFT or DFT.
    • Claim:
      14. The computer-implemented method of claim 11, wherein the isoform of the ion channel is one of a sodium channel (Nav), a potassium channel (Kv), a calcium channel (Cav), or a chloride channel (ClC).
    • Claim:
      15. The computer-implemented method of claim 11, wherein the single channel activity signal is measured using a cell-attached patch-clamp system or an ion conductance microscopy-guided smart patch-clamp system.
    • Claim:
      16. The computer-implemented method of claim 11, wherein performing the frequency-domain analysis on the single channel activity signal further comprises extracting at least one frequency-domain feature, the computer-implemented method further comprising: inputting, into a trained machine learning model, the at least one frequency-domain feature; and predicting, using the trained machine learning model, the isoform of the ion channel.
    • Claim:
      17. The computer-implemented method of claim 16, further comprising comparing the isoform of the ion channel identified based on the frequency-domain analysis to the isoform of the ion channel predicted by the trained machine learning model.
    • Claim:
      18. The computer-implemented method of claim 16, wherein the at least one frequency-domain feature comprises a power spectrum.
    • Claim:
      19. The computer-implemented method of claim 16, wherein the trained machine learning model is a supervised learning model.
    • Claim:
      20. The computer-implemented method of claim 19, wherein the supervised learning model is a decision tree classifier, a support vector machine (SVM), a k-nearest neighbors (KNN) classifier, a Naïve Bayes' classifier, or an artificial neural network.
    • Claim:
      21-33. (canceled)
    • Current International Class:
      16; 01
    • Accession Number:
      edspap.20240296910