BibTeX Export for A Graph-Based Similarity Approach to Classify Recurrent Complex Motifs from Their Context in RNA Structures

Copy to Clipboard Download

@InProceedings{gianfrotta_et_al:LIPIcs.SEA.2021.19,
  author =	{Gianfrotta, Coline and Reinharz, Vladimir and Barth, Dominique and Denise, Alain},
  title =	{{A Graph-Based Similarity Approach to Classify Recurrent Complex Motifs from Their Context in RNA Structures}},
  booktitle =	{19th International Symposium on Experimental Algorithms (SEA 2021)},
  pages =	{19:1--19:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-185-6},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{190},
  editor =	{Coudert, David and Natale, Emanuele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2021.19},
  URN =		{urn:nbn:de:0030-drops-137912},
  doi =		{10.4230/LIPIcs.SEA.2021.19},
  annote =	{Keywords: Graph similarity, clustering, RNA 3D folding, RNA motif}
}

The metadata provided by Dagstuhl Publishing on its webpages, as well as their export formats (such as XML or BibTeX) available at our website, is released under the CC0 1.0 Public Domain Dedication license. That is, you are free to copy, distribute, use, modify, transform, build upon, and produce derived works from our data, even for commercial purposes, all without asking permission. Of course, we are always happy if you provide a link to us as the source of the data.

Read the full CC0 1.0 legal code for the exact terms that apply: https://creativecommons.org/publicdomain/zero/1.0/legalcode