An automated framework for efficiently designing deep convolutional neural networks in genomics

Convolutional neural networks (CNNs) have become a standard for analysis of biological sequences. Tuning of network architectures is essential for a CNN’s performance, yet it requires substantial knowledge of machine learning and commitment of time and effort. This process thus imposes a major barri...

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Veröffentlicht in:	Nature machine intelligence 2021-05, Vol.3 (5), p.392-400
Hauptverfasser:	Zhang, Zijun, Park, Christopher Y., Theesfeld, Chandra L., Troyanskaya, Olga G.
Format:	Artikel
Sprache:	eng
Schlagworte:	631/114/1305 631/114/2397 631/208/177 Algorithms Artificial neural networks Automation Computer architecture Datasets Deep learning Design Engineering Genomes Genomics Machine learning Neural networks
Online-Zugang:	Volltext
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