dc.creator | Melgar García, Laura | es |
dc.creator | Gutiérrez Avilés, David | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.date.accessioned | 2022-04-06T07:52:32Z | |
dc.date.available | 2022-04-06T07:52:32Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Melgar García, L., Gutiérrez Avilés, D., Rubio Escudero, C. y Troncoso, A. (2020). High-Content Screening images streaming analysis using the STriGen methodology. En SAC 2020 : 35th Annual ACM Symposium on Applied Computing (537-539), Brno, Czech Republic: Association for Computing Machinery (ACM). | |
dc.identifier.isbn | 978-1-4503-6866-7 | es |
dc.identifier.uri | https://hdl.handle.net/11441/131784 | |
dc.description.abstract | One of the techniques that provides systematic insights into biolog ical processes is High-Content Screening (HCS). It measures cells
phenotypes simultaneously. When analysing these images, features
like fluorescent colour, shape, spatial distribution and interaction
between components can be found. STriGen, which works in the
real-time environment, leads to the possibility of studying time
evolution of these features in real-time. In addition, data stream ing algorithms are able to process flows of data in a fast way. In
this article, STriGen (Streaming Triclustering Genetic) algorithm
is presented and applied to HCS images. Results have proved that
STriGen finds quality triclusters in HCS images, adapts correctly
throughout time and is faster than re-computing the triclustering
algorithm each time a new data stream image arrives. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-88209-C2-1-R | es |
dc.description.sponsorship | TIN2017-88209-C2-2-R | es |
dc.format.extent | 3 | es |
dc.language.iso | eng | es |
dc.publisher | Association for Computing Machinery (ACM) | es |
dc.relation.ispartof | SAC 2020 : 35th Annual ACM Symposium on Applied Computing (2020), pp. 537-539. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Real-time | es |
dc.subject | Triclustering | es |
dc.subject | Genetic operators | es |
dc.subject | High-Content Screening | es |
dc.title | High-Content Screening images streaming analysis using the STriGen methodology | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2017-88209-C2-1-R | es |
dc.relation.projectID | TIN2017-88209-C2-2-R | es |
dc.relation.publisherversion | https://dl.acm.org/doi/10.1145/3341105.3374071 | es |
dc.identifier.doi | 10.1145/3341105.3374071 | es |
dc.publication.initialPage | 537 | es |
dc.publication.endPage | 539 | es |
dc.eventtitle | SAC 2020 : 35th Annual ACM Symposium on Applied Computing | es |
dc.eventinstitution | Brno, Czech Republic | es |
dc.relation.publicationplace | New York, USA | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |