- this matrix can be associated to each data sequence and the similarity between two sequences can be evaluated with the definition of a distance between the corresponding Hasse matrices;
- examples of sequential data are mass spectrometry signals, IR/UV signals, 1D – NMR spectra, electronic nose signals, proteomic maps, DNA sequencies, sequential molecular descriptors. In general, all the spectra achieved along time are intrinsically ordered and can be analysed as sequential data.
- the new proposed distance (weighted standardized Hasse distance) is evaluated between pairs of Hasse matrices derived from the classical partial ordering rules. It can be naturally standardized, thus allowing the interpretation of these distances as absolute values (e.g. percentage) and deriving simple similarity and correlation indices;
Conditions and warranty
The toolbox is freeware and may be used (but not modified) if proper reference is given to the authors. Preferably refer to the followign papers:
Todeschini, R., Consonni, V., Mauri, A., Ballabio, D. (2006). Characterization of DNA primary sequences by a new similarity/diversity measure based on the partial ordering. Journal of chemical information and modeling, 46, 1905-1911
In short, no guarantees, whatsoever, are given for the quality of this toolbox or for the consequences of its use. It is inevitable that there will be some bugs, but we have tried to test the algorithms thoroughly.
Download
Fill in the following form. Your personal data will be used only for notification via email of new releases of the toolbox and will not be communicated to external third parties. Once the form has been submitted, download and open the zip file and extract all the Matlab modules in a unique folder. Then, you can open Matlab, select the folder and type “help hasse“. This will display all the information you need to run the MATLAB modules..