Mónica G. Larese

Learn More
Bad quality spots should be filtered out at early steps in microarray analysis to avoid noisy data. In this paper we implement quality control of individual spots from real microarray images. First of all, we consider the binary classification problem of detecting bad quality spots. We propose the use of ensemble algorithms to perform detection and obtain(More)
Gene Set Enrichment Analysis (GSEA) is a well-known technique used for studying groups of functionally related genes and their correlation with phenotype. This method creates a ranked list of genes, which is used to calculate an enrichment score. In this work, we introduce two different metrics for gene ranking in GSEA, namely the Wilcoxon and the(More)
In this paper we propose an automatic algorithm able to classify legume leaf images considering only the leaf venation patterns (leaf shape, color and texture are excluded). This method processes leaf images captured with a standard scanner and segments the veins using the Unconstrained Hit-or-Miss Transform (UHMT) and adaptive threshold-ing. We measure(More)
In recent years, the importance of analyzing the effect of genetic variations on the plant phenotypes has raised much attention. In this paper, we describe a procedure which can be useful to discover representative leaf vein patterns for each species or variety under analysis. We consider three legumes, namely red bean, white bean and soybean. Soybean(More)