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

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https://doi.org/10.1007/3-540-27502-9ng the impact of the microbiome on the pathogenesis and progression of various diseases within the host. A deep learning tool, PopPhy-CNN, has been developed for the task of predicting host phenotypes using a convolutional neural network (CNN). By representing samples as annotated taxonomic trees an

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Anwendungen von Femtosekundenlasern,s the identification of hot spots (HS) at protein–protein interfaces, typically conserved residues that contribute most significantly to the binding. In this chapter, we depict point-by-point an in-house pipeline used for HS prediction using only sequence-based features from the well-known SpotOn da

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Klassifizierung von Femtosekundenlasern,h its sequence. We show that a partial combination of the Levenberg–Marquardt algorithm and the back-propagation algorithm produced the best results, giving the lowest error and largest Pearson correlation coefficient. We also find, as previous studies, that adding associative memory to a neural net

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Klassifizierung von Femtosekundenlasern,unts of labeled data. This chapter focuses on the prerequisite steps to the training of any algorithm: data collection and labeling. In particular, we tackle how data collection can be set up with scalability and security to avoid costly and delaying bottlenecks. Unprecedented amounts of data are no

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Artificial Neural Networks978-1-0716-0826-5Series ISSN 1064-3745 Series E-ISSN 1940-6029

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Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning,ng 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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Siamese Neural Networks: An Overview,aches can be used, depending on the final goal of the comparison (Euclidean distance, Pearson correlation coefficient, Spearman’s rank correlation coefficient, and others). But if the comparison has to be applied to more complex data samples, with features having different dimensionality and types w