Neural network model for integration and visualization of introgressed genome and metabolite data. - Georgina Stegmayer; Diego H. Milone; Laura Kamenetzky; Mariana G. Lopez; Fernando Carrari (IJCNN, 2009)

Métodos de agrupamiento no supervisado para la integración de datos genómicos y metabólicos de múltiples líneas de introgresión. - Diego H. Milone; Georgina Stegmayer; Matias Gerard; Laura Kamenetzky; Mariana G. Lopez; Fernando Carrari (Inteligencia Artif., 2009)

*omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants. - Diego H. Milone; Georgina Stegmayer; Laura Kamenetzky; Mariana G. Lopez; Je Min Lee; James J. Giovannoni; Fernando Carrari (BMC Bioinform., 2010)

A Biologically Inspired Validity Measure for Comparison of Clustering Methods over Metabolic Data Sets. - Georgina Stegmayer; Diego H. Milone; Laura Kamenetzky; Mariana G. Lopez; Fernando Carrari (IEEE ACM Trans. Comput. Biol. Bioinform., 2012)

Clustering biological data with SOMs: On topology preservation in non-linear dimensional reduction. - Diego H. Milone; Georgina Stegmayer; Laura Kamenetzky; Mariana G. Lopez; Fernando Carrari (Expert Syst. Appl., 2013)

Improving clustering with metabolic pathway data. - Diego H. Milone; Georgina Stegmayer; Mariana G. Lopez; Laura Kamenetzky; Fernando Carrari (BMC Bioinform., 2014)

Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization. - Milton Pividori; Andres Cernadas; Luis A. de Haro; Fernando Carrari; Georgina Stegmayer; Diego H. Milone (Bioinform., 2019)