Publications by authors named "Maria Kesa"
Article Synopsis
- The text discusses the challenges in analyzing large-scale neural data and highlights the importance of visualization methods to identify activity patterns in neurons.* -
- It introduces 'Rastermap', a new visualization technique that organizes neurons based on their activity patterns and allows researchers to explore recordings from various animal models, including mice and zebrafish.* -
- The effectiveness of Rastermap is benchmarked through simulations, and the text also notes that there are specific high-dimensional scenarios where this method and similar algorithms may fail to provide clear insights.*
Objective: High-throughput technologies have generated an unprecedented amount of high-dimensional gene expression data. Algorithmic approaches could be extremely useful to distill information and derive compact interpretable representations of the statistical patterns present in the data. This paper proposes a mining approach to extract an informative representation of gene expression profiles based on a generative model called the Counting Grid (CG).
View Article and Find Full Text PDFMapping brains on grids of features for schizophrenia analysis.
Med Image Comput Comput Assist Interv
January 2015
This paper exploits the embedding provided by the counting grid model and proposes a framework for the classification and the analysis of brain MRI images. Each brain, encoded by a count of local features, is mapped into a window on a grid of feature distributions. Similar sample are mapped in close proximity on the grid and their commonalities in their feature distributions are reflected in the overlap of windows on the grid.
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