Clustering has been one of the most popular methods to discover useful biological insights from DNA microarray. An interesting paradigm is simultaneous clustering of both genes and experiments. This "biclustering" paradigm aims at discovering clusters that consist of a subset of the genes showing a coherent expression pattern over a subset of conditions. The pClustering approach is a technique that belongs to this paradigm. Despite many theoretical advantages, this technique has been rarely applied to actual gene expression data analysis. Possible reasons include the worst-case complexity of the clustering algorithm and the difficulty in interpreting clustering results. In this paper, we propose an enhanced framework for performing pClustering on actual gene expression analysis. Our new framework includes an effective data preparation method, highly scalable clustering strategies, and an intuitive result interpretation scheme. The experimental result confirms the effectiveness of our approach.
Some kind of pollutant, particularly hydrocarbon, can be removed from the polluted subsoil by means of micro organism activity.
Oxygen is required during the biodegradation process and therefore air is injected in the subsoil.
A multiphase and multicomponent mathematical model describing the biodegradation process in a unsaturated porous media will be presented.
The movement of the different fluids in the porous media and the dynamic of the bacteria population will be described.
