Open AccessA WaveNet based Ion Channel prediction Method
Author(s) -
Yukun Teng,
Deheng Chen,
Yichun Zhou,
Nathaniel M. Vegh,
Ren Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012009
Subject(s) - ion channel , computer science , artificial intelligence , memorization , machine learning , speech recognition , chemistry , mathematics , mathematics education , biochemistry , receptor
There are many diseases associated with the dysfunction of ion channels. Ion channels are a class of proteins embedded in cell membranes that serve principle physiological functions such as memorization, learning and pain signaling. In order to help scientists learn more about ion channels and how they cause diseases, a team in Liverpool University published a public dataset about Ion channels. This paper presents a Wavenet based model to predict the number of Ion channels open at each time point. Wavenet is a type of deep neural network designed for one-dimensional signals. We used the Kalman filter to denoise the original signals and compared WaveNet with classical machine learning methods like SVM and Naïve Bayes to show the superior performance of the WaveNet model.