Internal Cluster Research Articles

A consensus multi-view multi-objective gene selection approach for improved sample classification

BackgroundIn the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different ‘omics’ resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are biased towards considering only one out of multiple biological resources. Consequently, some crucial aspects of available biological knowledge may get ignored, which could further improve feature selection efficiency.ResultsIn this present work, we have proposed a Consensus Multi-View Multi-objective Clustering-based feature selection algorithm called CMVMC. Three controlled genomic and proteomic resources like gene expression, Gene Ontology (GO), and protein-protein interaction network (PPIN) are utilized to build two independent views. The concept of multi-objective consensus clustering has been applied within our proposed gene selection method to satisfy both incorporated views. Gene expression data sets of Multiple tissues and Yeast from two different organisms (Homo Sapiens and Saccharomyces cerevisiae, respectively) are chosen for experimental purposes. As the end-product of CMVMC, a reduced set of relevant and non-redundant genes are found for each chosen data set. These genes finally participate in an effective sample classification.ConclusionsThe experimental study on chosen data sets shows that our proposed feature-selection method improves the sample classification accuracy and reduces the gene-space up to a significant level. In the case of Multiple Tissues data set, CMVMC reduces the number of genes (features) from 5565 to 41, with 92.73% of sample classification accuracy. For Yeast data set, the number of genes got reduced to 10 from 2884, with 95.84% sample classification accuracy. Two internal cluster validity indices - Silhouette and Davies-Bouldin (DB) and one external validity index Classification Accuracy (CA) are chosen for comparative study. Reported results are further validated through well-known biological significance test and visualization tool.

4O10Cd Clustering in ZnBVI-Rich CdxZn1−xOyBVI1−y (BVI = S, Se, Te) Highly Lattice-Mismatched Alloys

Clustering of highly lattice-mismatched semiconductor alloys has attracted considerable interest recently. However, strong internal strains and cluster variety are the main disadvantages of such alloys. Zinc blende ZnBVI-rich CdxZn1−xOyB1−yVI (BVI = S, Se, Te) with 4O10Cd identical clusters are proposed to resolve these problems. The decrease in the internal strains is a cause of the cluster formation in ZnS- and ZnSe-rich alloys. The favorable CdO and ZnTe bonding is an additional reason for the cluster occurrence in ZnTe-rich alloys. The cohesive energies of CdSe and ZnSe, stiffness coefficients, bond stretching and bond bending elastic constants of CdSe are calculated. The majority of Cd and oxygen atoms should be in 4O10Cd clusters up to 800°C if the ZnS- and ZnSe-rich alloy contents are, respectively, y ≥ 2.5 × 10−4 and y > 3 × 10−4, and x ≈ 2.5y. The obtained results demonstrate that CdxZn1−xOyB1−yVI materials are promising semiconductors with significantly reduced internal strains and identical polyatomic clusters containing oxygen tetrahedrons.

Visualization Analysis of Seismic Facies Based on Deep Embedded SOM

As a classical visualization tool for seismic facies analysis, the clustering process of self-organizing map (SOM) is generally divided into two stages: feature extraction and clustering. However, when the horizon pickings are ambiguous and waveforms are chaotic, the structural feature extraction cannot correspond well to the topological structure of SOM. To improve the performance of classification, we propose a one-stage method of deep embedded SOM (DESOM) for seismic facies visualization analysis, which means that the extracting feature representation and clustering are completed simultaneously. Moreover, the stability of the DESOM results can be improved by adding sparse constraints, and thus the visualization results can be displayed in more detail. In the experiment, by comparing with the external indexes of clustering and the hierarchical results of prototype categories, the superiorities of the DESOM and sparse DESOM (SDESOM) methods are verified based on a geophysical model. In the field data application, these methods are combined with the Hue, Saturation, Value (HSV) color mapping technology to display the geological structure information of the target horizon. According to the law of the correlation of facies and the internal cluster indexes, it approves that the DESOM method can improve the continuity of channels, and the SDESOM method can obtain more detailed information of seismic facies distribution.

Analysis of Medical Data Using Community Detection on Inferred Networks.

Performing network-based analysis on medical and biological data makes a wide variety of machine learning tools available. Clustering, which can be used for classification, presents opportunities for identifying hard-to-reach groups for the development of customized health interventions. Due to a desire to convert abundant DNA gene co-expression data into networks, many graph inference methods have been developed. Likewise there are many clustering and classification tools. This paper presents a comparison of techniques for graph inference and clustering, using different numbers of features, in order to select the best tuple of graph inference method, clustering method, and number of features according to a particular phenotype. An extensive machine learning based analysis of the REGARDS dataset is conducted, evaluating the CoNet and K-Nearest Neighbors (KNN) network inference methods, along with the Louvain, Leiden and NBR-Clust clustering techniques. Results from analysis involving five internal cluster evaluation indices show the traditional KNN inference method and NBR-Clust and Louvain clustering produce the most promising clusters with medical phenotype data. It is also shown that visualization can aid in interpreting the clusters, and that the clusters produced can identify meaningful groups indicating customized interventions.

GROUPING OF PROVINCES IN INDONESIA BASED ON MATERNAL HEALTH INDICATORS USING VARIABLE WEIGHTING K-MEANS AND FUZZY C-MEANS

Maternal Mortality Rate (MMR) is an indicator used to assess maternal health as well as the health status of a country. MMR is a target that must be achieved by Indonesian Government in Sustainable Development Goals (SDGs) in 2030. The Government of Indonesia has made various efforts to reduce MMR. This study aims to determine the distribution pattern of indicators for improving maternal health by grouping provinces in Indonesia based on the characteristics of maternal health indicators. The variables used are indicators that affect maternal mortality, namely K4 coverage (x1), Td2+ immunization coverage (x2), maternity assisted by health workers in health facilities coverage (x3), post-partum check up coverage (x4), Puskesmas implementing pregnant classes (x5), Puskesmas implementing P4K (x6), participant of KB coverage (x7) in Indonesia in 2017. The grouping methods are Variable Weighting K-Means (VWKM) and Fuzzy C-Means (FCM). The selection of the best grouping results uses the Internal Cluster Dispersion Rate (icdrate). Based on the analysis results, the best grouping is generated by the FCM method. The icdrate value generated by FCM is 0.325 while the icdrate value generated by VWKM is 0.552. FCM produces five groups which can be categorized as groups with maternal health indicator characteristics with very low, low, medium, high, and very high scores. Provinces in a group tend to be geographically close. East Java and Bali are provinces included in the indicator group of very high maternal health. Papua and West Papua fall into the group for maternal health which is very low.

Validation of Parkinson's Disease-Related Questionnaires in South Africa.

Background There are very few epidemiological studies investigating Parkinson's disease (PD) in Africa. The hundreds of local languages and dialects make traditional screening and clinical evaluation tools difficult to use. Objective The objective of the study was to validate two commonly used PD questionnaires in an African population. Methods The PD Screening Questionnaire (PDSQ) and Parkinson's Disease Questionnaire (PDQ-39) were modified and translated into Afrikaans, Setswana, and isiZulu and administered to a sample of healthy local residents. We assessed the internal consistencies and cluster characteristics of the questionnaires, using a Cronbach's alpha test and exploratory factor analysis. The questionnaires were then administered to a population-based sample of 416 research participants. We evaluated the correlations between the questionnaires and both a timed motor task and the Unified Parkinson's Disease Rating Scale motor subsection 3 (UPDRS3), using locally weighted scatterplot smoothing (LOWESS) regression analysis and Spearman's rank correlation. Results Both questionnaires had high overall internal consistency (Cronbach's alpha = 0.86 and 0.95, respectively). The modified PDQ-39 had evidence of five subscales, with Factor 1 explaining 57% and Factor 2 explaining 14%, of the variance in responses. The PDSQ and PDQ-39 scores were correlated with the UPDRS3 score (ρ = 0.35, P < 0.001; and ρ = 0.28, P < 0.001, respectively). Conclusion The translated PDSQ and PDQ-39 questionnaires demonstrated high internal consistency and correlations with clinical severity of parkinsonism and a timed motor task, suggesting that they are valid tools for field-based epidemiological studies.

Simultaneous feature selection and clustering of micro-array and RNA-sequence gene expression data using multiobjective optimization

In this paper, we have devised a multiobjective optimization solution framework for solving the problem of gene expression data clustering in reduced feature space. Here clustering problem is viewed from two different aspects: clustering of genes in reduced sample space or clustering of samples in reduced gene space. Three objective functions: two internal cluster validity indices and the count on the number of features are optimized simultaneously by a popular multiobjective simulated annealing based approach, namely AMOSA. Here, point symmetry based distance is used for the assignment of gene data points to different clusters. Seven publicly available benchmark gene expression data sets are used for experimental purpose. Both aspects of clustering in reduced feature space is demonstrated. The proposed gene expression clustering technique outperforms the existing nine clustering techniques. Apart from this, also some statistical and biological significant tests have been carried out to show that the proposed FSC-MOO technique is more statistically and biologically enriched

Identification of a conserved var gene in different Plasmodium falciparum strains

BackgroundThe multicopy var gene family of Plasmodium falciparum is of crucial importance for pathogenesis and antigenic variation. So far only var2csa, the var gene responsible for placental malaria, was found to be highly conserved among all P. falciparum strains. Here, a new conserved 3D7 var gene (PF3D7_0617400) is identified in several field isolates.MethodsDNA sequencing, transcriptional analysis, Cluster of Differentiation (CD) 36-receptor binding, indirect immunofluorescence with PF3D7_0617400-antibodies and quantification of surface reactivity against semi-immune sera were used to characterize an NF54 clone and a Gabonese field isolate clone (MOA C3) transcribing the gene. A population of 714 whole genome sequenced parasites was analysed to characterize the conservation of the locus in African and Asian isolates. The genetic diversity of two var2csa fragments was compared with the genetic diversity of 57 microsatellites fragments in field isolates.ResultsPFGA01_060022400 was identified in a Gabonese parasite isolate (MOA) from a chronic infection and found to be 99% identical with PF3D7_0617400 of the 3D7 genome strain. Transcriptional analysis and immunofluorescence showed expression of the gene in an NF54 and a MOA clone but CD36 binding assays and surface reactivity to semi-immune sera differed markedly in the two clones. Long-read Pacific bioscience whole genome sequencing showed that PFGA01_060022400 is located in the internal cluster of chromosome 6. The full length PFGA01_060022400 was detected in 36 of 714 P. falciparum isolates and 500 bp fragments were identified in more than 100 isolates. var2csa was in parts highly conserved (He = 0) but in other parts as variable (He = 0.86) as the 57 microsatellites markers (He = 0.8).ConclusionsIndividual var gene sequences exhibit conservation in the global parasite population suggesting that purifying selection may limit overall genetic diversity of some var genes. Notably, field and laboratory isolates expressing the same var gene exhibit markedly different phenotypes.

Gaussian mixture model based clustering of Manual muscle testing grades using surface Electromyogram signals

Muscle strength testing has long been an important assessment procedure in rehabilitation setups, though the subjectivity and standardization of this procedure has been widely debated. To address this issue, this study involves the use of Electromyogram (EMG) features that are intuitively related to muscle strength to classifyManual muscle testing (MMT)grades of '4-', '4', '4 + ' and '5' of the Medical Research Council scale. MMTwas performed on Tibialis anterior muscle of 50 healthy participants whose MMT grades and EMG were simultaneously acquired. Chi square goodness of fit and Spectrum Decomposition of Graph Laplacian (SPEC) feature selection algorithms areused in selecting five features, namely Integrated EMG, Root Mean Square EMG, Waveform Length, Wilsons' amplitude and Energy. Gaussian Mixture Model (GMM) approach is used for unsupervised clustering into one of the grades. Internal cluster evaluation resulted in Silhouette score of 0.76 and Davies Bouldin Index of 0.42 indicating good cluster separability. Agreement between the machine-based grade and manual grade has been quantified using Cohens' Kappa coefficient. A value of '0.44' has revealed a moderate agreement, with greater differences reported in grading '4' and '4 + ' strength levels. The comparative advantage of EMG based grading over the manual method has been proved. The suggested method can be extended for muscle strength testing of all muscles across different age groups to assist physicians in evaluating patient strength and plan appropriate strength conditioning exercises as a part of rehabilitative assessment.

A novel internal cluster validity index

A novel internal cluster validity index

U2O@C76: Non-Isolated-Pentagon-Rule Cages Prevail with the U2O Configuration Determined by Cage Shape and Dominated by Multicenter Bonds.

Endohedral clusterfullerenes (ECFs) are fullerene cages with various metallic clusters trapped inside. So far, the actinide-based ECFs are rather scarce with their possible structures and chemistry remaining largely unexplored. Herein, density functional theory calculations characterized that the recently synthesized U2O@C76 could be U2O@Cs(17 490)-C76 or U2O@C2v(19 138)-C76, whose cages have two or one pentagon adjacencies (PAs) and thus both violate the isolated pentagon rule (IPR). It is noteworthy that they are the first actinide-based ECFs bearing non-IPR outer cages. They are also the first Cs(17 490)- and C2v(19 138)-C76-based oxide ECFs. Moreover, U2O@C2v(19 138)-C76 is the first example of a hexavalent metal cluster within the C2v(19 138)-C76 cage. Interestingly, although trapped by the two same-sized cages, the U2O unit exhibits a bent and a perfect linear configuration, respectively, indicative of the crucial role of cage shape in steering the internal cluster configuration. Their electronic structures can be formally described as (U2O)6+@C766- with primary electrostatic attractions and secondary covalent interactions between cluster and cage. Significantly, bonding analyses reveal that the encaged U2O moiety may only features two three-center, two-electron (3c-2e) U-O-U bonds with completely absent common two-center bonds.

Longitudinal Tracking of Students’ Academic Progress using Model-based Clustering

Clustering methods have often been used to group similar students’ performance for tracking their academic progress. A typical approach is to group the students by treating the observations as accumulated average marks on several assessed subjects within a period of time. Here, similar characteristics of students are identified based on the overall variation of marks between the subjects but ignores the temporal aspect of students performance even though the assessments are carried out at different time points. Alternatively, such characteristics in the observations could be treated as a set of longitudinal data since the measurements consider time-spaced and repeated events. This paper aims to compare the output between these two different treatments of observations using Model-based Clustering (MBC). Specifically, the average observations are applied to the classical MBC whereas the longitudinal observations require some adjustment to the covariance matrix to cater for its longitudinal data structure. A synthetic data set generated based on some pre-university students’ marks on four science subjects from three series of continuous assessments are applied to the methods. The results show that the longitudinal observations on adjusted MBC produce a greater number of clusters that could characterise students’ progress with a better internal and external cluster quality compared to the average observations on classical MBC.

Discovery of a Superatom inside the Fullerene Cage.

The stability of endohedral clusterfullerenes is generally understood in terms of the metal cluster shape, cage structure, and metal-cage interactions, with the electronic state of the internal cluster mostly neglected. Herein, theoretical calculations reveal that the (Ti3C3)6+ unit of recently synthesized Ti3C3@Ih(7)-C80 exhibits a superatomic state with a perfect closed-shell 1S21P61D10 electronic configuration in accordance with the famous jellium model. This "trapped superatom" features considerable aromaticity and hyperconjugation interactions never reported for other clusterfullerenes. Besides the localized two-center two-electron (2c-2e) Ti-C/C-C bonds, it also has two 3c-2e Ti-C-Ti bonds. Furthermore, the ring strain of the cyclopropane-like C3 core is effectively released upon the metal coordination. All these factors greatly stabilize the (Ti3C3)6+ cluster, showing the critical role of metal-to-cage charge transfer and cage encapsulation in enhancing the stability of this exotic metal cluster.

A Refined 3-in-1 Fused Protein Similarity Measure: Application in Threshold-Free Hub Detection.

An exhaustive literature survey shows that finding protein/gene similarity is an important step towards solving widespread bioinformatics problems, such as predicting protein-protein interactions, analyzing Protein-Protein Interaction Networks (PPINs), gene prioritization, and disease gene/protein detection. In this article, we have proposed an improved 3-in-1 fused protein similarity measure called FuSim-II. It is built upon combining the weighted average of biological knowledge extracted from three potential genomic/ proteomic resources such as Gene Ontology (GO), PPIN, and protein sequence. Furthermore, we have shown the application of the proposed measure in detecting potential hub-proteins from a given PPIN. Aiming that, we have proposed a multi-objective clustering-based protein hub detection framework with FuSim-II working as the underlying proximity measure. The PPINs of H. Sapiens and M. Musculus organisms are chosen for experimental purposes. Unlike most of the existing hub-detection methods, the proposed technique does not require to follow any protein degree cut-off or threshold to define hubs. A thorough assessment of efficiency between proposed and existing eight protein similarity measures along with eight single/multi-objective clustering methods has been carried out. Internal cluster validity indices like Silhouette and Davies Bouldin (DB) are deployed to accomplish analytical study. Also, a comparative performance analysis between proposed and five existing hub-proteins detection algorithms is conducted through the enrichment of essentiality study. The reported results show the improved performance of FuSim-II over existing protein similarity measures in terms of identifying functionally related proteins as well as relevant hub-proteins. Supplementary material is available at http://csse.szu.edu.cn/staff/cuilz/eng/index.html.

Regioselective Synthesis, Crystallographic Characterization, and Electrochemical Properties of Pyrazole- and Pyrrole-Ring-Fused Derivatives of Y2 @C3v (8)-C82.

Chemical modification of endohedral metallofullerenes (EMFs) is an efficient strategy to realize their ultimate applications in many fields. Herein, we report the highly regioselective and quantitative mono-formation of pyrazole- and pyrrole-ring-fused derivatives of the prototypical di-EMF Y2 @C3v (8)-C82 , that is, Y2 @C3v (8)-C82 (C13 N2 H10 ) and Y2 @C3v (8)-C82 (C9 NH11 ), from the respective 1,3-dipolar reactions with either diphenylnitrilimine or N-benzylazomethine ylide, without the formation of any bis- or multi-adducts. Crystallographic results unambiguously reveal that only one [6,6]-bond out of the twenty-five different types of nonequivalent C-C bonds of Y2 @C3v (8)-C82 is involved in the 1,3-dipolar reactions. Our theoretical results rationalize that the remarkably high regioselectivity and the quantitative formation of mono-adducts are direct results from the anisotropic distribution of π-electron density on the C3v (8)-C82 cage and the local strain of the cage carbon atoms as well. Interestingly, electrochemical and theoretical studies demonstrate that the reversibility of the redox processes, in particular the reversibility of the reductive processes of Y2 @C3v (8)-C82 , has been markedly altered upon exohedral functionalization, but the oxidative process was less influenced, indicating that the oxidation is mainly influenced by the internal Y2 cluster, whereas the reduction is primarily associated with the fullerene cage. The pyrazole and pyrrole-fused derivatives may find potential applications as organic photovoltaic materials and biological reagents.

ПРОЄКТНА СХЕМА ЦИКЛУ УПРАВЛІННЯ ІНТЕГРОВАНИМИ ЗУСИЛЛЯМИ З РОЗВИТКУ ПІДПРИЄМСТВ КЛАСТЕРУ ТУРИЗМУ ТА РЕКРЕАЦІЇ

Introduction. The authors have proposals outline specification management cycle of integrated effort for the development of business clusters. Tourism and Recreation, based on general features area-specific management.Purpose. The goal of the work was consistent definition management features integrated effort the development of business in clusters Tourism and Recreation.Results. The authors were requested to design, plan the management cycle of integrated effort for the development of business in clusters Tourism and Recreation. That draft contain:1) synthesis, analysis and diagnostic portrait of the cluster, surrounding the geographical, historical, demographic, economic, social characteristic, supported by assessment state of tourist attractions (as a functioning of the environment);2) mission and common vision, which gave operational meaning a promising integrated сluster development, mission each of autonomous members;3) macro-micro analysis, competitive advantage and restrictions future development, integrative environments of cluster;4) identified the strategic direction activities, singled out as population, strategic objectives and provisional action, relatively achieve the goals: a description of the actions, target dates for the implementation, responsible persons, sources of funding; implementation mechanisms strategic plan; the list of participants formulation.Originality. Оn based on the use of comparisons, structural, historical, logical, Systems Analysis; secondary data analysis, integrating, interpretation, project methods being developed іnstrumental time dimension of management process the cascading based on a system of medium-term and ongoing (budget) plans. The provision devoted with what basis for planning cannot be only an optimistic expectation, it is necessary a holistic scientific basis of problem that may arise in implementing the proposed areas development integration of business in clusters Tourism and Recreation.Conclusion. The study is based on a use of comparisons, historical, logical, systems analysis; secondary data analysis, integrating, interpretation, project methods. The scientific approach lies in design of scheme management cycle integrated effort for the development of business in Tourism and Recreation clusters. Following the study conclusions were made about the availability, multi и business environment inside of a Tourism and Recreation clusters. Therefore, strategic analysis is an integrated multi-level conversion of the database, generated by analysis of the external (macro) and internal (micro) cluster environment and internal environments party's autonomous (Microanalysis) and in the future is the basis for strategic map and identification success factor. The further scientific development to define the basic criteria and form the specifics of building an effective governance paradigm the development of Tourism and Recreation clusters.

Weighted Cluster Ensemble Based on Partition Relevance Analysis With Reduction Step

Over the last decade, the advent of the cluster ensemble framework has enabled more accurate and robust data analysis than traditional single clustering algorithms. The improved clustering of microarray data has had a particularly strong impact in the fields of genomics and medicine. However, when we bring several ensemble members together to form a consensus, low-quality data partitions can seriously compromise the final solution. One way to overcome this problem is the weighted cluster ensemble approach based on Partition Relevance Analysis (PRA), which uses internal cluster validity indices to evaluate and weight the ensemble members before the fusion. Unfortunately, the selection of appropriate validation indices for given data is far from trivial. In this paper, we propose an additional step in PRA that reduces the size of the committee of cluster validation indices. It does so by eliminating redundant and noisy indices using data dimensionality reduction methods. Our extension works in an unsupervised way, minimizing the amount of user intervention and required expert knowledge. We adapted three conventional consensus functions based on the principle of evidence accumulation to work with PRA weights. We demonstrate the advantages of the proposed reduction step of PRA based on extensive experiments with 25 gene expression and 15 non-genetic real-world datasets, where we compared 15 consensus functions. The source code is available at https://github.com/nejci/PRAr.

Inelastic light scattering in the spin cluster Mott insulator Cu2OSeO3

Clusters of single spins form the relevant spin entities in the formation of long-range magnetic order in spin cluster Mott insulators. Such type of spin order bears resemblance to molecular crystals, and we therefore may expect a prototypical spin wave spectrum which can be divided into low-energy external and high-energy internal cluster spin wave modes. Here, we study high-energy spin cluster excitations in the spin cluster Mott insulator Cu$_{2}$OSeO$_{3}$ by means of spontaneous Raman scattering. Multiple high-energy optical magnon modes are observed, of which the Raman-activity is shown to originate in the Elliot-Loudon scattering mechanism. Upon crossing the long-range ordering transition temperature the magnetic modes significantly broaden, corresponding to scattering from localized spin excitations within the spin clusters. Different optical phonon modes show a strong temperature dependence, evidencing a strong magnetoelectric coupling between optical phonons and the high-energy spin cluster excitations. Our results support the picture that Cu$_{2}$OSeO$_{3}$ can be regarded as a solid-state molecular crystal of spin nature.

A Hierarchical Clustering algorithm based on Silhouette Index for cancer subtype discovery from genomic data

Identifying potential novel subtypes of cancers from genomic data requires techniques to estimate the number of natural clusters in the data. Determining the number of natural clusters in a dataset has been a challenging problem in Machine Learning. Employing an internal cluster validity index such as Silhouette Index together with a clustering algorithm has been a widely used technique for estimating the number of natural clusters, which has limitations. We propose a Hierarchical Agglomerative Clustering algorithm which automatically estimates the numbers of natural clusters and gives the associated clustering solutions along with dendrograms for visualizing the clustering structure. The algorithm has a Silhouette Index-based criterion for selecting the pair of clusters to merge, in the process of building the clustering hierarchy. The proposed algorithm could find decent estimates for the number of natural clusters, and the associated clustering solutions when applied to a collection of cancer gene expression datasets and general datasets. The proposed method showed better overall performance when compared to that of a set of prominent methods for estimating the number of natural clusters, which are used for cancer subtype discovery from genomic data. The proposed method is deterministic. It can be a better alternative to contemporary approaches for identifying potential novel subtypes of cancers from genomic data.

Evaluation of the Effectiveness of Financing Industrial Clusters

The article is solving an actual problem — development of a system of indicators to evaluate the effectiveness of financing industrial clusters in Russia. The article analyzes the cluster models of Russian and foreign authors, identifies their strengths and weaknesses. A universal information model of the cluster was developed, reflecting the interaction of the participants among themselves and with external stakeholders of the cluster development. The developed model has three control loops: internal cluster stakeholders, cluster, cluster's region. Each has the specificity of the movement of inventory and cash flows, information interaction in the implementation of cluster policy, and reflects the interests of various stakeholders of industrial clusters. The model lays the groundwork to justify a three-tier system of indicators to evaluate the effectiveness of financing industrial clusters. The subsystems of the indicators of the impact of the industrial cluster on the regional economy, of the indicators of the industrial cluster development and the subsystem of the indicators of the financial condition of enterprises participating in the industrial cluster are highlighted in the proposed system. The study used the methods of bibliographic and logical analysis, synthesis and systems approach, mathematical methods of statistical data processing. The developed system of indicators for assessing the effectiveness of financing industrial clusters can be used to conduct current and subsequent monitoring of financing the implementation of cluster programs, to prepare decisions on the allocation of budgetary funds by state and municipal authorities, and to potential investors to determine the most promising investment instruments.