Biodiversidad, Evolución y Conservación

URI permanente para esta comunidad

https://hdl.handle.net/10784/26725

Examinar

Examinando Biodiversidad, Evolución y Conservación por Autor "Ariza-Jiménez L."

Mostrando 1 - 2 de 2
  • No hay miniatura disponible
    Ítem
    An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data
    (SPRINGER, 2019-10-01) Ariza-Jiménez L.; Pinel N.; Villa L.F.; Quintero O.L.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y Conservación
    Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups. © 2020, Springer Nature Switzerland AG.
  • No hay miniatura disponible
    Ítem
    Standardized Approaches for Assessing Metagenomic Contig Binning Performance from Barnes-Hut t-Stochastic Neighbor Embeddings
    (SPRINGER, 2020-01-01) Ceballos J.; Ariza-Jiménez L.; Pinel N.; Ceballos J.; Ariza-Jiménez L.; Pinel N.; Universidad EAFIT. Departamento de Ciencias; Bioiversidad, Evolución y Conservación
    The performance of unsupervised methods for metagenomic binning is often assessed using simulated microbial communities. The lack of well-characterized evaluation protocols and approaches to community construction cognizant of biological realities impedes the rigorous assessment and standardization of the binning process. This work attempted to standardize performance evaluation using benchmark communities constructed according to the genome similarity metric Average Amino Acid identity. This approach allowed us to extend and deepen our previous research on the unsupervised binning of metagenomic sequence fragments based on low-dimensional embeddings of pentamer frequency profiles. Experimental results evidenced our method’s potential for the binning of metagenomic contigs to become an alternative to state-of-the-art methods such as MetaCluster 3.0. © 2020, Springer Nature Switzerland AG.