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"Soft windowing application to improve analysis of high-throughput ..."
Hamed Haselimashhadi et al. (2020)
- Hamed Haselimashhadi, Jeremy C. Mason
, Violeta Muñoz-Fuentes
, Federico López-Gómez
, Kolawole O. Babalola
, Elif F. Acar, Vivek Kumar, Jacqui White, Ann M. Flenniken, Ruairidh King, Ewan Straiton
, John Richard Seavitt, Angelina Gaspero, Arturo Garza, Audrey E. Christianson, Chih-Wei Hsu
, Corey L. Reynolds, Denise G. Lanza
, Isabel Lorenzo, Jennie R. Green, Juan J. Gallegos, Ritu Bohat, Rodney C. Samaco, Surabi Veeraragavan, Jong Kyoung Kim
, Gregor Miller
, Helmut Fuchs, Lillian Garrett, Lore Becker
, Yeon Kyung Kang, David Clary, Soo Young Cho, Masaru Tamura, Nobuhiko Tanaka, Kyung Dong Soo, Alexandr Bezginov, Ghina Bou About, Marie-France Champy, Laurent Vasseur, Sophie Leblanc, Hamid Meziane, Mohammed Selloum
, Patrick T. Reilly, Nadine Spielmann, Holger Maier, Valérie Gailus-Durner, Tania Sorg
, Hiroshi Masuya, Yuichi Obata, Jason D. Heaney, Mary E. Dickinson, Wolfgang Wurst, Glauco P. Tocchini-Valentini, Kevin C. Kent Lloyd, Colin McKerlie, Je Kyung Seong
, Yann Herault
, Martin Hrabé de Angelis
, Steve D. M. Brown, Damian Smedley, Paul Flicek
, Ann-Marie Mallon
, Helen E. Parkinson
, Terrence F. Meehan
:
Soft windowing application to improve analysis of high-throughput phenotyping data. Bioinform. 36(5): 1492-1500 (2020)

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