@article{pereaSW1PerSSlidingWindows2015,
  title = {{{SW1PerS}}: {{Sliding}} Windows and 1-Persistence Scoring; Discovering Periodicity in Gene Expression Time Series Data},
  volume = {16},
  issn = {1471-2105},
  url = {https://doi.org/10.1186/s12859-015-0645-6},
  doi = {10.1186/s12859-015-0645-6},
  shorttitle = {{{SW1PerS}}},
  abstract = {Identifying periodically expressed genes across different processes (e.g. the cell and metabolic cycles, circadian rhythms, etc) is a central problem in computational biology. Biological time series may contain (multiple) unknown signal shapes of systemic relevance, imperfections like noise, damping, and trending, or limited sampling density. While there exist methods for detecting periodicity, their design biases (e.g. toward a specific signal shape) can limit their applicability in one or more of these situations.},
  journaltitle = {BMC Bioinformatics},
  shortjournal = {BMC Bioinformatics},
  urldate = {2018-05-02},
  date = {2015-08-16},
  pages = {257},
  keywords = {Gene expression,Periodicity,Persistent homology,Sliding windows,Time series},
  author = {Perea, Jose A. and Deckard, Anastasia and Haase, Steve B. and Harer, John},
  file = {/home/dimitri/Nextcloud/Zotero/storage/YH7DK289/Perea et al. - 2015 - SW1PerS Sliding windows and 1-persistence scoring.pdf;/home/dimitri/Nextcloud/Zotero/storage/2N8XI4AJ/s12859-015-0645-6.html}
}

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