Titlebook: Deep Learning for Biomedical Data Analysis; Techniques, Approach Mourad Elloumi Book 2021 Springer Nature Switzerland AG 2021 Deep Learning

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https://doi.org/10.1007/978-3-030-71676-9Deep Learning (DL); Biomedical Data Analysis; Biomedical Image Analysis; Medical Diagnostics; Artificial

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Nobukazu Nakagoshi,Jhonamie A. Mabuhaynscriptional level and cause translational inhibition or mRNA cleavage. Quick and effective detection of the binding sites of miRNAs is a major problem in bioinformatics. This chapter introduces a new technique to model microRNA-target binding using . (RNN) over a miRNA-target duplex sequence representation.

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1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Datadata, which has a large number of features. DNA microarray technology is an approach to monitor the expression levels of sizable genes simultaneously. Microarray gene expression data is more useful for predicting and understanding various diseases such as cancer. Most of the microarray data are beli

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Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Ialysis is represented by sequence classification, a methodology that is widely used to analyze sequential data of different nature. However, its application to DNA sequences requires a proper representation of such sequences, which is still an open research problem. . (ML) methodologies have given a

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