Deep learning neural networks are capable to extract significant features from raw data, and to use these features for classification tasks. In this work we present a deep learning neural network for DNA sequence classification based on spectral sequence representation. The framework is tested on a dataset of 3000 16S genes and compared to the GRNN that we tested outperform the Support Vector Machine classification algorithm.
A deep learning approach to DNA sequence classification: first results
Riccardo Rizzo;Antonino Fiannaca;Massimo La Rosa;Alfonso Urso
2015
Abstract
Deep learning neural networks are capable to extract significant features from raw data, and to use these features for classification tasks. In this work we present a deep learning neural network for DNA sequence classification based on spectral sequence representation. The framework is tested on a dataset of 3000 16S genes and compared to the GRNN that we tested outperform the Support Vector Machine classification algorithm.File in questo prodotto:
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