Examinando por Autor "Pinel N."
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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 ConservaciónUnsupervised 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.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; Ciencias Biológicas y Bioprocesos (CIBIOP)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.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. Escuela de Ciencias; Modelado MatemáticoUnsupervised 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.Ítem Extracted information quality, a comparative study in high and low dimensions(Inderscience Enterprises Ltd., 2020-12-11) Ariza-Jimenez L.; Villa, LF; Pinel N.; Lucia Quintero M, O.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y ConservaciónÍtem Extracted information quality, a comparative study in high and low dimensions(Inderscience Enterprises Ltd.) Ariza-Jimenez L.; Villa, LF; Pinel N.; Lucia Quintero M, O.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)Ítem HIPAE helicopter-borne in-situ pollution assessment experiment: Plataforma alternativa para la medición de contaminantes en capas verticales(Institute of Electrical and Electronics Engineers Inc., 2019-01-01) Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Universidad EAFIT. Departamento de Ciencias; Bioiversidad, Evolución y ConservaciónÍtem HIPAE helicopter-borne in-situ pollution assessment experiment: Plataforma alternativa para la medición de contaminantes en capas verticales(Institute of Electrical and Electronics Engineers Inc., 2019-01-01) Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)Ítem HIPAE helicopter-borne in-situ pollution assessment experiment: Plataforma alternativa para la medición de contaminantes en capas verticales(Institute of Electrical and Electronics Engineers Inc., 2019-01-01) Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Universidad EAFIT. Departamento de Ciencias; Electromagnetismo Aplicado (Gema)[No abstract available]Ítem HIPAE helicopter-borne in-situ pollution assessment experiment: Plataforma alternativa para la medición de contaminantes en capas verticales(Institute of Electrical and Electronics Engineers Inc., 2019-01-01) Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Universidad EAFIT. Departamento de Ciencias; Modelado MatemáticoÍtem HIPAE helicopter-borne in-situ pollution assessment experiment: Plataforma alternativa para la medición de contaminantes en capas verticales(Institute of Electrical and Electronics Engineers Inc., 2019-01-01) Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.Ítem HIPAE helicopter-borne in-situ pollution assessment experiment: Plataforma alternativa para la medición de contaminantes en capas verticales(Institute of Electrical and Electronics Engineers Inc., 2019-01-01) Botero A.Y.; Florez J.; Duque J.F.; Rendon A.; Lopez-Restrepo S.; Pinel N.; Quintero O.L.; Oquinte1@eafit.edu.co; Rodriguez J.S.; Galvez J.; Lopera D.V.; Montilla E.; Marulanda J.I.; Isaza C.; Lainez M.L.A.; Zapata A.F.; Universidad EAFIT. Departamento de Ciencias Básicas; Óptica AplicadaÍtem 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 ConservaciónThe 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.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; Ciencias Biológicas y Bioprocesos (CIBIOP)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.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; Modelado MatemáticoThe 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.Ítem Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings(Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Ariza-Jimenez L.; Quintero O.L.; Pinel N.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y ConservaciónShotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.Ítem Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings(Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Ariza-Jimenez L.; Quintero O.L.; Pinel N.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)Shotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.Ítem Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic Neighbor Embeddings(Institute of Electrical and Electronics Engineers Inc., 2018-01-01) Ariza-Jimenez L.; Quintero O.L.; Pinel N.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoShotgun metagenomic studies attempt to reconstruct population genome sequences from complex microbial communities. In some traditional genome demarcation approaches, high-dimensional sequence data are embedded into two-dimensional spaces and subsequently binned into candidate genomic populations. One such approach uses a combination of the Barnes-Hut approximation and the t -Stochastic Neighbor Embedding (BH-SNE) algorithm for dimensionality reduction of DNA sequence data pentamer profiles; and demarcation of groups based on Gaussian mixture models within humanimposed boundaries. We found that genome demarcation from three-dimensional BH-SNE embeddings consistently results in more accurate binnings than 2-D embeddings. We further addressed the lack of a priori population number information by developing an unsupervised binning approach based on the Subtractive and Fuzzy c-means (FCM) clustering algorithms combined with internal clustering validity indices. Lastly, we addressed the subject of shared membership of individual data objects in a mixed community by assigning a degree of membership to individual objects using the FCM algorithm, and discriminated between confidently binned and uncertain sequence data objects from the community for subsequent biological interpretation. The binning of metagenome sequence fragments according to thresholds in the degree of membership opens the door for the identification of horizontally transferred elements and other genomic regions of uncertain assignment in which biologically meaningful information resides. The reported approach improves the unsupervised genome demarcation of populations within complex communities, increases the confidence in the coherence of the binned elements, and enables the identification of evolutionary processes ignored in hard-binning approaches in shotgun metagenomic studies. © 2018 IEEE.