Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Aurelio López Fernández"'
Autor:
Aurelio López-Fernández, Francisco A. Gómez-Vela, Jorge Gonzalez-Dominguez, Parameshachari Bidare-Divakarachari
Publikováno v:
SoftwareX, Vol 26, Iss , Pp 101666- (2024)
BioScience is an advanced Python library designed to satisfy the growing data analysis needs in the field of bioinformatics by leveraging High-Performance Computing (HPC). This library encompasses a vast multitude of functionalities, from loading spe
Externí odkaz:
https://doaj.org/article/09003b6308614628a782e786c4492361
Autor:
Dulcenombre M. Saz-Navarro, Aurelio López-Fernández, Francisco A. Gómez-Vela, Domingo S. Rodriguez-Baena
Publikováno v:
SoftwareX, Vol 25, Iss , Pp 101634- (2024)
The construction of gene co-expression networks is an essential tool in Bioinformatics for discovering useful biological knowledge. There are a multitude of methodologies related to the construction of this type of network, and one of them is EnGNet,
Externí odkaz:
https://doaj.org/article/1f8999d8fc414b4e8672a16e70285e28
Autor:
Julia Figueroa-Martínez, Dulcenombre M. Saz-Navarro, Aurelio López-Fernández, Domingo S. Rodríguez-Baena, Francisco A. Gómez-Vela
Publikováno v:
Informatics, Vol 11, Iss 2, p 14 (2024)
Gene networks have become a powerful tool for the comprehensive examination of gene expression patterns. Thanks to these networks generated by means of inference algorithms, it is possible to study different biological processes and even identify new
Externí odkaz:
https://doaj.org/article/fe6cb6ff0571422693f10fc887bebfce
Autor:
Francisco Gómez-Vela, Aurelio López-Fernández, Federico Divina, Miguel García-Torres, Domingo S. Rodriguez-Baena
Publikováno v:
Journal of Parallel and Distributed Computing. 147:209-219
Graphics Processing Units technology (GPU) and CUDA architecture are one of the most used options to adapt machine learning techniques to the huge amounts of complex data that are currently generated. Biclustering techniques are useful for discoverin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9fcbdeb800948c051e1999f97984974b
https://doi.org/10.1016/j.jpdc.2020.09.009
https://doi.org/10.1016/j.jpdc.2020.09.009
Publikováno v:
RIO. Repositorio Institucional Olavide
instname
Digital Scholarship in the Humanities, fqaa021
RIO: Repositorio Institucional Olavide
Universidad Pablo de Olavide (UPO)
Digital Scholarship in the Humanities
instname
Digital Scholarship in the Humanities, fqaa021
RIO: Repositorio Institucional Olavide
Universidad Pablo de Olavide (UPO)
Digital Scholarship in the Humanities
This article presents a relational database capable of integrating data from a variety of types of written sources as well as material remains. In response to historical research questions, information from such diverse sources as documentary, bioant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37fcf358efafd3d9cf7a7951df10f872
http://hdl.handle.net/10433/10734
http://hdl.handle.net/10433/10734
Publikováno v:
Electronics, Vol 9, Iss 1782, p 1782 (2020)
Electronics
Volume 9
Issue 11
Electronics
Volume 9
Issue 11
Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1deceda3c8aa031aed1e74463e842caa
https://doi.org/10.3390/electronics9111782
https://doi.org/10.3390/electronics9111782
Autor:
Francisco Gómez-Vela, Aurelio López-Fernández, Domingo S. Rodriguez-Baena, Norberto Díaz-Díaz
Publikováno v:
Computational biology and chemistry. 76
Background and objective Gene enrichment tools enable the analysis of the relationships between genes with biological annotations stored in biological databases. The results obtained by these tools are usually difficult to analyse. Therefore, researc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ba563d210fd6980536a1a4ea637997a
https://pubmed.ncbi.nlm.nih.gov/30029028
https://pubmed.ncbi.nlm.nih.gov/30029028