Molecular classification of borderline ovarian tumors using hierarchical cluster analysis of protein expression profiles

2002 ◽  
Vol 98 (6) ◽  
pp. 895-899 ◽  
Author(s):  
Ayodele A. Alaiya ◽  
Bo Franzén ◽  
Anders Hagman ◽  
Bjarte Dysvik ◽  
Uwe J. Roblick ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Protein Expression ◽  
Hierarchical Cluster Analysis ◽  
Expression Profiles ◽  
Hierarchical Cluster ◽  
Molecular Classification ◽  
Ovarian Tumors ◽  
Borderline Ovarian Tumors ◽  
Protein Expression Profiles

Unsupervised and hierarchical cluster analysis and classification of SAR images

1993 ◽  
Author(s):  
Yiu-fai Wong ◽  
Kenneth J. Peters ◽  
Edward C. Poser
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Sar Images

scholarly journals Classification of Red Wines Produced from Zweigelt and Rondo Grape Varieties Based on the Analysis of Phenolic Compounds by UPLC-PDA-MS/MS

Molecules ◽  
2020 ◽  
Vol 25 (6) ◽  
pp. 1342 ◽  
Author(s):  
Anna Stój ◽  
Ireneusz Kapusta ◽  
Dorota Domagała
Keyword(s):  
Cluster Analysis ◽  
Phenolic Compounds ◽  
Vitis Vinifera ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Malolactic Fermentation ◽  
Red Wines ◽  
Grape Variety ◽  
Grape Varieties

The authentication of grape variety from which wine is produced is necessary for protecting a consumer from adulteration and false labelling. The aim of this study was to analyze phenolic compounds in red monovarietal wines produced from Zweigelt (Vitis vinifera) and Rondo (non-Vitis vinifera) varieties while using the UPLC-PDA-MS/MS method and to assess whether these wines can be classified according to grape variety that is based on chemometric analysis. Fifty-five phenolic compounds belonging to five classes—anthocyanins, flavonols, flavan-3-ols, phenolic acids, and stilbenes—were identified and quantified in Zweigelt and Rondo wines. The wines of the Zweigelt variety were characterized by lower concentrations of phenolic compounds than those of the Rondo variety. Furthermore, wines of the Zweigelt variety contained the highest concentrations of flavan-3-ols, and wines of the Rondo variety—the highest concentrations of anthocyanins. Hierarchical cluster analysis (HCA) revealed that Zweigelt wines and Rondo wines formed two separate groups. The Rondo group was divided into two subgroups, differing in type of malolactic fermentation (spontaneous or induced). Phenolic compounds analysis by means of UPLC-PDA-MS/MS combined with HCA is a useful tool for the classification of red wines that were produced from Zweigelt and Rondo grape varieties, regardless of yeast strain and type of malolactic fermentation.

Proteomic Analysis Identifies Outcome-Predictive Clusters in Patients with Peripheral T-Cell Lymphoma, Not Otherwise Specified

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1623-1623
Author(s):  
Maja Ludvigsen ◽  
Martin Bjerregaard Pedersen ◽  
Stephen Jacques Hamilton-Dutoit ◽  
Knud Bendix ◽  
Michael Boe Møller ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
T Cell ◽  
Protein Expression ◽  
Proteomic Analysis ◽  
Cell Lymphoma ◽  
Expression Profiles ◽  
Peripheral T Cell Lymphoma ◽  
Not Otherwise Specified ◽  
Protein Expression Profiles ◽  
Unsupervised Cluster Analysis

Abstract Introduction: Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) is a heterogeneous group of mature T-cell lymphomas, probably composed by different biologically related subsets that have not yet been conclusively identified. In the WHO classification, PTCL-NOS accounts for 25-30% of all mature T-/NK-cell malignancies. The clinical outcome is generally poor with a 5-yr overall survival of 30-35% after conventional treatment strategies. The aim of the study was to apply proteomic analysis in PTCL-NOS and to use the protein expression profiles to characterize clinically relevant subsets within this heterogeneous entity by means of unsupervised cluster analysis. Methods: Archival frozen tumor tissue samples from 20 patients diagnosed with PTCL-NOS from 1991 to 2010 were analyzed for protein expression by high-resolution two-dimensional gel electrophoresis. Individual protein spots were visualized with fluorescence staining and the expression profiles were identified. All patients were homogeneously treated with curatively intended anthracycline-containing combination regimens. Clinico-pathological features were obtained from the Danish Lymphoma Registry (LYFO) and from patient records. Hyperplastic tonsils from healthy adults were included as reference tissue (n=8). Principal component analysis and unsupervised hierarchical cluster analysis were performed on the basis of the protein expression profiles. Differentially expressed (two-fold or higher, Mann-Whitney U-test) proteins between the detected clusters were identified by liquid chromatography - tandem mass spectrometry. Results: Unsupervised cluster analysis defined three distinct clusters: one containing all reference samples and two additional ones further subdividing the PTCL-NOS cases in two separate subsets. Patients from these two PTCL-NOS subsets had significantly different responses to treatment and survival (p = 0.001). The differentially expressed proteins were primarily involved in (i) promotion of tumor growth, (ii) regulation of cellular metabolism, and (iii) immune responses. Conclusion : Proteomic analysis identified shared protein expression patterns and potential prognostic markers in subsets of PTCL-NOS. Disclosures No relevant conflicts of interest to declare.

scholarly journals Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles

2015 ◽  
Vol 14 (11) ◽  
pp. 2947-2960 ◽  
Author(s):  
Sally J. Deeb ◽  
Stefka Tyanova ◽  
Michael Hummel ◽  
Marc Schmidt-Supprian ◽  
Juergen Cox ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Expression ◽  
B Cell ◽  
Cell Lymphoma ◽  
Expression Profiles ◽  
B Cell Lymphoma ◽  
Large B Cell Lymphoma ◽  
Protein Expression Profiles ◽  
Large B Cell

scholarly journals Peripheral areas and their distinctive characteristics: The case of Hungary

2021 ◽  
Vol 29 (3) ◽  
pp. 217-230
Author(s):  
János Pénzes ◽  
Gábor Demeter
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Development Policies ◽  
Relevant Variables ◽  
Peripheral Areas

Abstract The delimitation and classification of peripheral settlements using multivariate statistical methods is presented in this article, with a case study of Hungary. A combination of four different methods provided the basis for the delimitation of settlements defined as peripheral. As significant overlapping was detected between the results of the different methods, peripheries – more than one-fifth of the Hungarian settlements – were identified in a common set of the results. The independence of the results from the applied methods points to the fact that peripherisation is multi-faceted, and the peripheries of Hungary are stable and well-discernible from other regions. After the identification of peripheral areas, we classified these settlements into groups based on their specific features. Multiple steps specifying the relevant variables resulted in selecting the most appropriate 10 indicators and these served as the basis for a hierarchical cluster analysis, through which 7 clusters (types of peripheries) were identified. Five of them comprised enough cases to detect the most important dimensions and specific features of the backwardness of these groups. These clusters demonstrated a spatial pattern and their socioeconomic and infrastructural features highlighted considerable disparities. These differences should be taken into consideration when development policies are applied at regional levels or below.

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