Modeling and Theoretical Analysis of Artificial Neural Networks for Life Sciences

Dear Colleagues,

Artificial neural networks are usually inspired by biological nervous systems, such as multilayer perceptron, feedback neural networks, convolutional neural networks, and spiking neural networks, which partly simulate the natural nervous system. Recently, artificial neural networks have been widely used in life sciences, and deep neural networks in particular have shown great vitality in many related fields, such as medical biology, bioinformatics, and computational biology. However, there are still some problems to be solved in artificial neural networks. First, more effective and powerful biomimetic neural networks are still required to address the application problems of life sciences, because there is still plenty of room to solve problems in the real world for the existing artificial neural networks that are far from being regarded as strong artificial intelligence. Second, theoretical analysis of existing artificial neural networks is urgently needed because most of them lack interpretability, meaning that we still cannot explain why neural networks make the decisions they make, which also limits their use in some situations. Third, a systematic study of modeling methods in the applications of artificial neural networks is needed because designing neural networks for specific tasks is still expensive, and we need universal modules and modeling methods to help engineers to make better use of them in life sciences. Fourth, research in adversarial attacks for deep neural networks has shown that there is great destructive power in their applications, and well-designed adversarial examples can fool deep neural-network-based systems. Thus, research on attack and defense techniques for deep neural networks is urgent for them to be applied in life sciences. This Special Issue focuses on new ideas, methods, and techniques in modeling and theoretical analysis of artificial neural networks for life sciences, and related application research on neural networks is also welcome.

Dr. Xin Jin
Guest Editor

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