It is a flexible and creative means of storing, managing, and querying of complex biological datasets.
It is important to further develop and test methodologies to extract reliable information-flow networks from biological datasets.
BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.
FLSOM was compared with other SOM-based algorithms, using both artificial and real biological datasets [8, 14].
In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data.
Therefore, those methods mostly focused on the integration of various biological datasets to obtain more accurate similarity.
However, the compendium of chemical biological datasets generated by experimentalists and available in publicly-accessible repositories is skyrocketing [1].
For the efficient application of machine learning however, biological datasets need to become more systematic, more precise – and much larger.
A simulated dataset and three real biological datasets are used to test the validity of the FNT model.
Identifying patterns of association or dependency among high-dimensional biological datasets with sparse precision matrices remains a challenge.
The efficiency and interpretation of the methodology is illustrated by its application to artificial, benchmark and real complex biological datasets.
