A Machine Learning Framework for the Identification of Ion Channel Isoforms Via Stochastic Gating Signatures
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, cilt.2026, sa.1, ss.1-12, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 2026 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1155/int/6767530
- Dergi Adı: INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
- Derginin Tarandığı İndeksler: Applied Science & Technology Source, Engineering Source (EBSCO), Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Aerospace Database, Science Citation Index Expanded (SCI-EXPANDED), Compendex, INSPEC, zbMATH
- Sayfa Sayıları: ss.1-12
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
Accurate and automated discrimination of ion channel isoforms from ionic current recordings is a critical challenge in biophysicalanalysis and drug discovery. In this study, a high-accuracy classifcation structure integrating time–frequency signal represen-tations with machine learning techniques is proposed to address this problem. To efectively capture the nonstationary nature ofionic currents, short-time Fourier transform (STFT) is applied to the raw time-domain recordings of two representative sodiumchannel isoforms (Nav1.5 and Nav1.6). Through this transformation, 1D signals are converted into 2D time–frequency images. Adataset 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 classifer combinations are investigated. Experimental results dem-onstrate that local binary pattern (LBP) features provide superior discriminative capability for these representations. Specifcally,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 thepresented method provides a promising proof-of-concept framework for automated ion channel isoform classifcation, demon-strating the practical potential of STFT-based feature representations combined with ensemble learning under limited-dataconditions.