Pablo Vicente Munuera

Pablo Vicente Munuera
University of Seville | US · Cell Biology

MSc Bioinformatics

About

12
Publications
2,057
Reads
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173
Citations
Introduction
Computational biology. Ecosia Ambassador. Complex networks. Trying to help the environment.
Additional affiliations
September 2008 - August 2015
University of Alicante
Position
  • Student

Publications

Publications (12)
Article
Full-text available
Here we present EpiGraph, an image analysis tool that quantifies epithelial organization. Our method combines computational geometry and graph theory to measure the degree of order of any packed tissue. EpiGraph goes beyond the traditional polygon distribution analysis, capturing other organizational traits that improve the characterization of epit...
Article
Full-text available
Tumors are complex networks of constantly interacting elements: tumor cells, stromal cells, immune and stem cells, blood/lympathic vessels, nerve fibers and extracellular matrix components. These elements can influence their microenvironment through mechanical and physical signals to promote tumor cell growth. To get a better understanding of tumor...
Article
Full-text available
The original version of this Article contained an error in ref. 39, which incorrectly cited ‘Fristrom, D. & Fristrom, J. W. in The Development of Drosophila melanogaster (eds. Bate, M. & Martinez-Arias, A.) II, (Cold spring harbor laboratory press, 1993)’. The correct reference is ‘Condic, M.L, Fristrom, D. & Fristrom, J.W. Apical cell shape change...
Article
Full-text available
As animals develop, tissue bending contributes to shape the organs into complex three-dimensional structures. However, the architecture and packing of curved epithelia remains largely unknown. Here we show by means of mathematical modelling that cells in bent epithelia can undergo intercalations along the apico-basal axis. This phenomenon forces ce...
Conference Paper
In this paper we propose a method to topologically analyze segmented images of cells in a biological tissue. This is a mainly experimental paper in which we present initial results of applying persistent homology computation to characterize cell organization. For that aim, a graph is constructed to model the cell organization and a simplicial compl...

Questions

Question (1)
Question
Hey!
I get 2 geo dataset of RNA expression (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43696 and other), same platform same use, and both bronchial epithelial cells, and classes (control, mild moderate asthma, and severe asthma).
The aim of all of this was to classify as best as i could with supervised methods. Without FSS (feature subset selection), we got bad results. But, when we reduced both datasets we get AUC > 0.75 as overall. So, this filter is consistent and the sondes(genes) we get are important.
It is true (and biologically consistent) because we get genes like CPA3, WNK4, SCGB1A1.. Although, we find genes like TDRD5 (spermiogenesis), C12orf39 (agoraphobia), PLAC4 (placenta-especific), and syt13.
What could you say about this? Are they just some random genes? Or it could be logical to find those things?
If you want more information about the procedure i have a document about it.
Thanks in advance.

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