It has been found that in system biology studies, a lot of data in terms of heterogeneity and volume is getting accumulated. For example people studying botany, look at different measurements of morphological agronomic traits, various kinds of molecules (both metabolites and DNA or RNA molecules) as well as the choice of the consumer. This kind of data is highly variable and does not even have a linear relationship. Hence,there was a need for developing tool for dealing with such diverse data.
Thus, Pividori M. et. al. (2019) came up with Clustalmatch which can work with such diverse data and do the kind of datamining required. Comparison with other such tools show that Clustalmatch can work with complex data in a much more efficient manner.
The files required to implement it are available at the following URL: https://sourceforge.net/projects/sourcesinc/files/clustermatch/ and https://bitbucket.org/sinc-lab/clustermatch/ and the demo is available at http://sinc.unl.edu.ar/web-demo/clustermatch/
Reference:
Pividori M. et al.(2019) Clustermatch: Discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization. Bioinformatics 35(11):1931-1939.