Graph-based Clustering of miRNA Sequences.

Aims: The many miRNAs discovered so far have been divided into biologically representative families, aiming at organizing and systematizing their study so to promote, mainly, a better understanding of their functionalities. Clustering miRNA sequences can corroborate the family-based organizations as well as helping to explore sequences belonging to the same cluster as potentially having similar biological functions.

Observations: Considering that members of the same miRNA family tend to biologically function in similar ways, a well-structured family can help detecting miRNA functions which have not been associated yet with any existing family.

Attribute-based Decision Graphs: A framework for multiclass data classification.

Graph-based algorithms have been successfully applied in machine learning and data mining tasks. A simple but, widely used, approach to build graphs from vector-based data is to consider each data instance as a vertex and connecting pairs of it using a similarity measure. Although this abstraction presents some advantages, such as arbitrary shape representation of the original data, it is still tied to some drawbacks, for example, it is dependent on the choice of a pre-defined distance metric and is biased by the local information among data instances.

Quantitative Analysis of Rat Dorsal Root Ganglion Neurons Cultured on Microelectrode Arrays Based on Fluorescence Microscopy Image Processing.

Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification.