The analysis of large sets of biological data requires deep understanding of data mining concepts. To handle problems in the field of bioinformatics we use special algorithms, and extend existing solutions, or improve them to be more efficient.

    MCMC Network: Graphical Interface for Bayesian Analysis of Metabolic Networks

    Metabolic network Metabolic networks are under continuous evolution. Most organism have a common set of reactions as a part of their metabolic networks that relate to essential processes but a large proportion of the reactions present in different organisms are specific to the needs of individual organisms or tissues. The regions of metabolic networks corresponding to these non-essential reactions are under continuous evolution.

    Reticular Alignment: algorithm for multiple sequence alignment

    Reticular Alignment is our new method for for multiple sequence alignment. Unlike previous corner-cutting methods, our approach does not define a compact part of the dynamic programming table. Instead, it defines a set of optimal and suboptimal alignments at each step during the progressive alignment. The set of alignments are represented with a network to store them and use them during the progressive alignment in an efficient way.


    EU funded project to develop data mining tools for in silico drug discovery. Results: small molecule fingerprint model.

    Small molecule similarity modeling, Richter

    Small molecule similarity modeling and clustering for Richter Richter Gedeon Ltd. 2004-2006.

    Molecules are easy to be represented by labeled graphs. Unfortunately, this representation is not very suitable for computing similarity between molecules.
    Therefore, fingerprint models are applied to solve this problem much more efficiently.