Mostrar el registro sencillo de la publicación
A review of neural networks for metagenomic binning
| dc.contributor.author | Herazo-Álvarez, Jair | |
| dc.contributor.author | Mora, Marco | |
| dc.contributor.author | Cuadros-Orellana, Sara | |
| dc.contributor.author | Vilches-Ponce, Karina | |
| dc.contributor.author | Hernández-García, Ruber | |
| dc.date.accessioned | 2025-06-12T14:14:23Z | |
| dc.date.available | 2025-06-12T14:14:23Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/6122 | |
| dc.description.abstract | One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering of metagenomic sequences. Various machine learning models have been applied to address this task. In this review, the contributions of artificial neural networks (ANN) in the context of metagenomic binning are detailed, addressing both supervised, unsupervised, and semi-supervised approaches. 34 ANN-based binning tools are systematically compared, detailing their architectures, input features, datasets, advantages, disadvantages, and other relevant aspects. The findings reveal that deep learning approaches, such as convolutional neural networks and autoencoders, achieve higher accuracy and scalability than traditional methods. Gaps in benchmarking practices are highlighted, and future directions are proposed, including standardized datasets and optimization of architectures, for third-generation sequencing. This review provides support to researchers in identifying trends and selecting suitable tools for the metagenomic binning problem. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | Briefings in Bioinformatics, 26(2), bbaf065 | es_CL |
| dc.subject | Metagenomics | es_CL |
| dc.subject | Binning | es_CL |
| dc.subject | Neural networks | es_CL |
| dc.subject | Deep learning | es_CL |
| dc.subject | Classification | es_CL |
| dc.subject | Clustering | es_CL |
| dc.title | A review of neural networks for metagenomic binning | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.indexacion | Isi | es_CL |
| dc.ucm.uri | oxfordjournals.ucm.elogim.com/bib/article/26/2/bbaf065/8093116 | es_CL |
| dc.ucm.doi | doi.org/10.1093/bib/bbaf065 | es_CL |
Ficheros en la publicación
| Ficheros | Tamaño | Formato | Ver |
|---|---|---|---|
|
No hay ficheros asociados a esta publicación. |
|||
Esta publicación aparece en la(s) siguiente(s) colección(ones)
Excepto si se señala otra cosa, la licencia de la publicación se describe como Atribución-NoComercial-SinDerivadas 3.0 Chile