Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks
Tutkimustuotos › › vertaisarvioitu
Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017 |
| Kustantaja | Springer Verlag |
| Sivut | 555-558 |
| Sivumäärä | 4 |
| ISBN (painettu) | 9789811051210 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 2018 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) - Kesto: 1 tammikuuta 1900 → … |
Julkaisusarja
| Nimi | IFMBE Proceedings |
|---|---|
| Vuosikerta | 65 |
| ISSN (painettu) | 1680-0737 |
Conference
| Conference | Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
In this paper we study how a Convolutional Recurrent Neural Network performs for predicting the gene expression levels from histone modification signals. Moreover, we consider two simplified variants of the Convolutional Recurrent Neural Network: Convolutional Neural Network and Recurrent Neural Network. The performance of the methods is evaluated with histone modification signal and gene expression data derived from Roadmap Epigenomics Mapping Consortium database, and compared against the state of the art method: the DeepChrome. It is shown that the proposed models give a statistically significant improvement over the baseline.