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BACKGROUND: The nervous system senses and transmits information through the firing behavior of neurons, and this process is affected by various noises. However, in the previous study of the influence of noise on nerve discharge, the channel of some noise effects is not clear, and the difference from other noises was not examined. OBJECTIVE: To construct ion channel noise which is more biologically significant, and to clarify the basic characteristics of the random firing rhythm of neurons generated by different types of noise acting on ion channels. Method: Based on the dynamics of the ion channel, we constructed ion channel noise. We simulated the nerve discharge based on the Chay model of potassium ion channel noise, and used the nonlinear time series analysis method to measure the certainty and randomness of nerve discharge. RESULTS: In the Chay model with potassium ion noise, the chaotic rhythm defined by the original model could be effectively unified with the random rhythm simulated by the previous random Chay model into a periodic bifurcation process. CONCLUSION: This method clarified the influence of ion channel noise on nerve discharge, better understood the randomness of nerve discharge and provided a more reasonable explanation for the mechanism of nerve discharge.

BACKGROUND: For a protein to execute its function, ensuring its correct subcellular localization is essential. In addition to biological experiments, bioinformatics is widely used to predict and determine the subcellular localization of proteins. However, single-feature extraction methods cannot effectively handle the huge amount of data and multisite localization of proteins. Thus, we developed a pseudo amino acid composition (PseAAC) method and an entropy density technique to extract feature fusion information from subcellular multisite proteins. OBJECTIVE: Predicting multiplex protein subcellular localization and achieve high prediction accuracy. METHOD: To improve the efficiency of predicting multiplex protein subcellular localization, we used the multi-label k-nearest neighbors algorithm and assigned different weights to various attributes. The method was evaluated using several performance metrics with a dataset consisting of protein sequences with single-site and multisite subcellular localizations. RESULTS: Evaluation experiments showed that the proposed method significantly improves the optimal overall accuracy rate of multiplex protein subcellular localization. CONCLUSION: This method can help to more comprehensively predict protein subcellular localization toward better understanding protein function, thereby bridging the gap between theory and application toward improved identification and monitoring of drug targets.

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