The structure of a gene network reveals 7 biological sub-graphs underlying eQTLs in pig

Finally, we have tested the optimal combination and parameters determined for the first se- quence on the two others sequences of the BOSS European project (with other types of

Indeed, Louvain is sys- tematically the best method w.r.t modularity, while having a relatively bad internal clustering coefficient.. On the other hand, Conclude is very good with

For these two categories of documents, graph based representations can be used to describe the image content (e.g. region adjacency graph [3] for graphical and

Unlike the popular polynomial graph filters, which are based on a single graph shift operator, we consider convex combinations of graph shift operators particularly adapted to

These results show that an adequate combination of statistical methods, namely inference of graph using partial correlation under a Gaussian model, followed by clustering of graph,

Bollob´ as and Riordan showed [3] that the expectation of the clustering coefficient of the model from [4] is asymptotically proportional to (log n) 2 /n.. Bollob´ as and Riordan

Three main results are worth noting: first, key genes were highlighted as a potential focus for forth- coming biological experiments; second, a set of biological functions,

In order to be able to analyse data from different autonomic tests, two configurations, showing different levels of detail of the vascular system, have been proposed: a