Szczegóły publikacji
Opis bibliograficzny
EBIC.JL - an efficient implementation of evolutionary biclustering algorithm in Julia / Paweł Renc, Patryk ORZECHOWSKI, Aleksander BYRSKI, Jarosław WĄS, Jason H. Moore // W: GECCO'21 [Dokument elektroniczny] : Genetic and Evolutionary Computation Conference Companion : July 10-14, 2021, Lille, France : proceedings. — Wersja do Windows. — Dane tekstowe. — USA : Association for Computing Machinery, cop. 2021. — e-ISBN: 987-1-4503-8351-6. — S. 1540–1548. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://dl.acm.org/doi/pdf/10.1145/3449726.3463197 [2021-07-22]. — Bibliogr. s. 1547–1548, Abstr. — P. Orzechowski - dod. afiliacja: University of Pennsylvania, Philadelphia, USA
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 135355 |
|---|---|
| Data dodania do BaDAP | 2021-07-27 |
| DOI | 10.1145/3449726.3463197 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2021 |
Abstract
Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics. This technique has shown promise in multiple areas, including development of biomarkers for cancer, disease subtype identification, or gene-drug interactions among others. In this paper we introduce EBIC.JL - an implementation of one of the most accurate biclustering algorithms in Julia, a modern highly parallelizable programming language for data science. We show that the new version maintains comparable accuracy to its predecessor EBIC while converging faster for the majority of the problems. We hope that this open source software in a high-level programming language will foster research in this promising field of bioinformatics and expedite development of new biclustering methods for big data.
