Abstract
Single channel ionic current at several conductance states has been evaluated by a machine learning approach based on fuzzy clustering. Current jumps values have been clustered into an optimal number of well separated partitions using an original fuzzy validation index. This optimal partitions scheme allows to discriminate single channel classes discarding multi channels and spike due current evaluating and using of the proper open single channel noise. This method may be applied to step-like or burst-like single ionic current jumps. Specifically, burst-like single ionic current jumps generated by tetanus toxin (TeTx) have been studied. This case study represents a typical methodological validation example due to the presence of several spikes, current multi-channels and different conductance.
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Rauch, G., Ruggiero, C., Giacomini, M. (2016). Evaluating Single Channel Ionic Current by Fuzzy Clustering with a Partition Validation Index. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_87
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DOI: https://doi.org/10.1007/978-3-319-32703-7_87
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