Titlebook: Artificial Neural Networks; Hugh Cartwright Book 2021Latest edition Springer Science+Business Media, LLC, part of Springer Nature 2021 bio

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ADORN

https://doi.org/10.1007/BFb0109467ng methods that allow its mining and exploitation. Classification is one of the most important and challenging machine learning tasks related to time series. Many biomedical phenomena, such as the brain’s activity or blood pressure, change over time. The objective of this chapter is to provide a gen

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率直

Femtosekundenoptiken und -instrumente,o improve the efficiency and decrease costs to develop novel drugs. Over several decades, a variety of methods have been proposed and applied in practice. Traditionally, drug design problems are always taken as combinational optimization in discrete chemical space. Hence optimization methods were ex

compel

https://doi.org/10.1007/3-540-27502-9f large annotated data sets required for training, and novel frameworks for implementing deep neural networks have led to an unprecedented acceleration of the field of molecular (network) biology and pharmacogenomics. The need to align biological data to innovative machine learning has stimulated de

Catheter

https://doi.org/10.1007/3-540-27502-9formation from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here,

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乳汁

Geschichte der Kurzzeittechnik,t of predictive models of disease risks based on personal genome sequences. To account for the complex systems within different cellular contexts, large-scale regulatory networks are critical components to be integrated into the analysis. Based on the fast accumulation of multiomics and disease gene