A Machine Learning Framework for the Identification of Ion Channel Isoforms Via Stochastic Gating Signatures.
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| Title: | A Machine Learning Framework for the Identification of Ion Channel Isoforms Via Stochastic Gating Signatures. |
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| Authors: | Demirtaş, Mustafa1 (AUTHOR) mustafademirtas@uludag.edu.tr, Aksoy, Abdullah1 (AUTHOR), Yiğit, Enes1,2 (AUTHOR) |
| Source: | International Journal of Intelligent Systems. 8/30/2026, Vol. 2026, p1-12. 12p. |
| Subjects: | Time-frequency analysis, Ensemble learning, Classification, Feature extraction, Signal processing, Machine learning, Ion channels |
| Abstract: | Accurate and automated discrimination of ion channel isoforms from ionic current recordings is a critical challenge in biophysical analysis and drug discovery. In this study, a high‐accuracy classification structure integrating time–frequency signal representations with machine learning techniques is proposed to address this problem. To effectively capture the nonstationary nature of ionic currents, short‐time Fourier transform (STFT) is applied to the raw time‐domain recordings of two representative sodium channel isoforms (Nav1.5 and Nav1.6). Through this transformation, 1D signals are converted into 2D time–frequency images. A dataset comprising 845 STFT‐based images (420 for Nav1.5 and 425 for Nav1.6) is constructed for training and testing procedures. Various feature extraction strategies and machine learning classifier combinations are investigated. Experimental results demonstrate that local binary pattern (LBP) features provide superior discriminative capability for these representations. Specifically, an ensemble learning strategy combining bagged trees with LBP features achieved the highest and most consistent performance, yielding training, validation, and testing accuracy of 94.6%, 91.34%, and 94.49%, respectively. The results obtained reveal that the presented method provides a promising proof‐of‐concept framework for automated ion channel isoform classification, demonstrating the practical potential of STFT‐based feature representations combined with ensemble learning under limited‐data conditions. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | Accurate and automated discrimination of ion channel isoforms from ionic current recordings is a critical challenge in biophysical analysis and drug discovery. In this study, a high‐accuracy classification structure integrating time–frequency signal representations with machine learning techniques is proposed to address this problem. To effectively capture the nonstationary nature of ionic currents, short‐time Fourier transform (STFT) is applied to the raw time‐domain recordings of two representative sodium channel isoforms (Nav1.5 and Nav1.6). Through this transformation, 1D signals are converted into 2D time–frequency images. A dataset comprising 845 STFT‐based images (420 for Nav1.5 and 425 for Nav1.6) is constructed for training and testing procedures. Various feature extraction strategies and machine learning classifier combinations are investigated. Experimental results demonstrate that local binary pattern (LBP) features provide superior discriminative capability for these representations. Specifically, an ensemble learning strategy combining bagged trees with LBP features achieved the highest and most consistent performance, yielding training, validation, and testing accuracy of 94.6%, 91.34%, and 94.49%, respectively. The results obtained reveal that the presented method provides a promising proof‐of‐concept framework for automated ion channel isoform classification, demonstrating the practical potential of STFT‐based feature representations combined with ensemble learning under limited‐data conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 08848173 |
| DOI: | 10.1155/int/6767530 |
