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EnglishNucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid the features extraction and selection steps. We have computed classifications using eight datasets of three different organisms with a growing genome complexity, from yeast to human. We have also studied the capability of the model trained on the highest complex species in recognizing nucleosomes of the other organisms.
| Data di pubblicazione: | 2020 |
| Titolo: | Recurrent Deep Neural Networks for Nucleosome Classification |
| Autori: | |
| Citazione: | Amato, D., Di Gangi, M.A., Lo Bosco, G., & Rizzo, R. (2020). Recurrent Deep Neural Networks for Nucleosome Classification. In M. Raposo, P. Ribeiro, S. Sério, A. Staiano, & A. Ciaramella (a cura di), Computational Intelligence Methods for Bioinformatics and Biostatistics, 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018 (pp. 118-127). |
| Abstract: | Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid the features extraction and selection steps. We have computed classifications using eight datasets of three different organisms with a growing genome complexity, from yeast to human. We have also studied the capability of the model trained on the highest complex species in recognizing nucleosomes of the other organisms. |
| ISBN: | 978-3-030-34584-6 978-3-030-34585-3 |
| Digital Object Identifier (DOI): | 10.1007/978-3-030-34585-3_11 |
| Settore Scientifico Disciplinare: | Settore INF/01 - Informatica Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni |
| Appare nelle tipologie: | 2.07 Contributo in atti di convegno pubblicato in volume |
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