Sergii Babichev

Inductive model of data clustering based on the agglomerative hierarchical algorithm

Model of data clustering system based on the complex use of agglomerative hierarchical algorithm and inductive modeling methods of complex systems is presented in the paper. The quality of clustering is evaluated by two equal power subsets with the use of complex balance criterion, which takes into account both the displacement the mass centers of the appropriate clusters of different subsets and distribution of objects in the appropriate clusters relative to the mass center. Evaluating the effectiveness of the proposed model was performed using data sets containing clusters of different shapes. Charts of the external and internal complex criterion values against clustering level were created, which allows to determine the optimal clustering of a data set.

Criterial analysis of gene expression sequences to create the objective clustering inductive technology

The paper presents the researches to determine the effectiveness of different criteria to estimate the complex biology objects clustering quality. The gene expression sequences of cancer patients were used as experimental data. The degree of the studied objects similarity was estimated by the comparison of the gene expression sequences profile using different metrics to estimate the objects proximity. The studies have shown that the best separating ability is obtained by using the correlation metric proximity of objects. Herewith the use of the CH criterion (Calinski-Harabasz) allows to get the most objective objects clustering by using simulated data. The presented research is focused mainly on the inductive model of the objective clustering, where the objects clustering is carried out concurrently on the two equal power subsets. In this case, the final decision about the objects grouping is accepted using the two subsets basing both on the internal clustering quality criteria estimating and the minimum value of the external criterion of clustering similarity.

Objective Clustering Inductive Technology of Gene Expression Sequences Features

Technology of high dimensional data features objective clustering based on the methods of complex systems inductive modeling is presented in the paper. Architecture of the objective clustering inductive technology as a block diagram of step-by-step implementation of the objects clustering procedure was developed. Method of criterial evaluation of complex data clustering results using two equal power data subsets is proposed. Degree of clustering objectivity evaluates on the basis of complex use of internal and external criteria. Researches on the simulation results of the proposed technology based on the SOTA self-organizing clustering algorithm using the gene expression data obtained by DNA microarray analysis of patients with lung cancer GEOD-68571 Array Express database, the datasets “Compound” and “Aggregation” of the Computing School of the Eastern Finland University and the data “seeds” are presented.

Model of the Objective Clustering Inductive Technology of Gene Expression Profiles Based on SOTA and DBSCAN Clustering Algorithms

The paper presents the hybrid model of the objective clustering inductive technology based on complex using of the self-organizing SOTA and the density DBSCAN clustering algorithms. The inductive methods of complex systems analysis were used as the basis to implement the objective clustering inductive technology of gene expression profiles. To estimate the clustering quality for equal power subsets (include the same quantity of pairwise similar objects) the complex multiplicative criterion was calculated as the combination of the Calinski-Harabasz criterion and WB-index. The external clustering quality criterion is calculated as the normalized difference of the internal clustering quality criteria for the equal power subsets. The final decision concerning the determination of the optimal parameters of the clustering algorithm operation is done based on the maximum value of the Harrington desirability function that takes into account both the character of the objects and the clusters distribution in various clustering and the difference between clustering, which are implemented on the equal power subsets. The studied data grouping within the framework of the objective clustering inductive technology was performed in two stages. Firstly, the studied gene expression profiles were grouped with the use DBSCAN clustering algorithm. Then, the obtained set of gene expression profiles was divided into two clusters using SOTA clustering algorithm. This step-by-step procedure of the data clustering crates the conditions to save more useful information for following data processing.

A Fuzzy Model for Gene Expression Profiles Reducing Based on the Complex Use of Statistical Criteria and Shannon Entropy

The paper presents the technology of gene expression profiles reducing based on the complex use of fuzzy logic methods, statistical criteria and Shannon entropy. Simulation of the reducing process has been performed with the use of gene expression profiles of lung cancer patients. The variance and the average absolute value were changed within the defined range from the minimum to the maximum value, and Shannon entropy from the maximum to the minimum value during the simulation process. 311 gene expression profiles from 7129 were removed as non-informativity during simulation process. The structural block diagram of the step-by-step data processing in order to remove non-informativity gene expression profiles has been proposed as the simulation results.

Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles

We have developed a technique for the reconstruction and validation of models of gene networks based on the gene expression profiles derived in the course of DNA microchip experiments or by the method of RNA molecules sequencing. A structural block diagram is presented of a stepwise process for determining optimal parameters of the algorithm for reconstruction of a gene network that meet the optimum network topology. We proposed a comprehensive estimation criterion of a gene network topology based on the Harrington desirability function that contains network topological parameters as constituent components. The maximum value of this criterion corresponds to the optimal topology of a gene network. A technique for the validation of models of gene networks is based on a ROC analysis whose implementation implies a comparative analysis of the character of relations between relevant genes in the network on the basis of the totality of genes and gene networks based on the obtained biclusters. Qualitative reconstruction of a gene network makes it possible to explore the nature of interaction between genes that determine the process of functioning of a biological organism at different stages of development of complex genetic diseases for the purpose of early diagnosis and correction of a given process. It was established that the gene network reconstructed based on the correlation output algorithm is more efficient in comparison with the gene network based on the algorithm ARACNE. The weighted average of relative validation criterion for the derived models based on the correlation output algorithm is significantly greater than the corresponding value when applying the algorithm of ARACNE. This fact indicates a higher degree of compliance with the character of relations between respective genes in the network based on the totality of genes and in the networks based on gene expression profiles in the obtained biclusters. Qualitative reconstruction of a gene network makes it possible to explore the character of development of a biological organism at the gene level, which creates preconditions for early diagnosis and adjustment of the development of different types of genetic diseases.