Szczegóły publikacji
Opis bibliograficzny
EBIC: a scalable biclustering method for large scale data analysis / Patryk ORZECHOWSKI, Jason H. Moore // W: GECCO 2019 [Dokument elektroniczny] : the Genetic and Evolutionary Computation Conference : a recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24rd Annual Genetic Programming Conference (GP) : July 13th–17th 2019, Prague, Czech Republic. — Wersja do Windows. — Dane tekstowe. — USA : ACM, cop. 2019. — e-ISBN: 978-1-4503-6748-6. — S. 31–32. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://dl.acm.org/ft_gateway.cfm?id=3326762=2071089 [2019-09-18]. — Bibliogr. s. 32, Abstr. — P. Orzechowski – dod. afiliacja: University of Pennsylvania
Autorzy (2)
- AGHOrzechowski Patryk
- Moore Jason H.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 124345 |
|---|---|
| Data dodania do BaDAP | 2019-10-09 |
| DOI | 10.1145/3319619.3326762 |
| Rok publikacji | 2019 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computation Conference |
Abstract
Biclustering is a technique that looks for patterns hidden in some columns and some rows of the input data. Evolutionary search-based biclustering (EBIC) is probably the first biclustering method that combines high accuracy of detection of multiple patterns with support for big data. EBIC has been recently extended to a multi-GPU method and allows to analyze very large datasets. In this short paper, we discuss the scalability of EBIC as well as its suitability for RNA-seq and single cell RNA-seq (scRNA-seq) experiments.