Cluster analysis method in electrophoretic patterns classification

被引:0
|
作者
Spirovski, F. [1 ]
Stojanoski, K.
Mitrevski, A.
机构
[1] Univ St Cyril & Methudius, Fac Nat Sci, Inst Chem, Skopje 1000, Macedonia
[2] Univ St Cyril & Methudius, Fac Med, Neurol Clin, Skopje 1000, Macedonia
关键词
disc electrophoresis; cerebrospinal fluid; immunoglobulin G; cluster analysis;
D O I
10.1007/BF03245776
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The conventional electrophoresis methods are well known techniques for protein detection and analysis of cerebrospinal fluid (CSF). Disc electrophoresis (DEP) was carried out for detection of oligoclonal IgG bands in cerebrospinal fluid (CSF) on polyacrilamide gel. However, the advance of automation has made rapid collection of large amounts of data feasible and the development of microcomputers has made sophisticated processing even of old electrophoregrams possible. Automated analysis, data storage and sophisticate data acquisition were carried out with Gel Pro Analyzer 3.1, which is specifically structured to analyze gels and electrophoregrams: complex band pattern matching (gel variation, dendogram analysis etc.); lane relation studies (sophisticated lane database); general gel analysis (accurate molecular size, quantitative determination of protein mixture etc.). Clustering techniques have been applied for detection of intrathecal immune response. Different hierarchic cluster analysis methods such as single linkage, complete linkage, unweighted pair-group average (UPMGA) were used. In addition, other cluster characteristics such, distance matrix and Euclidean distance matrix were calculated. Pairing of electrophoresis methods and cluster image analysis, could lead to additional diagnostic information of inflammatory conditions of the central nervous system (CNS) or dysfunction of blood-CSF barrier.
引用
收藏
页码:26 / 31
页数:6
相关论文
共 50 条
  • [1] Cluster analysis method in electrophoretic patterns classification
    F. Spirovski
    K. Stojanoski
    A. Mitrevski
    Journal of the Iranian Chemical Society, 2005, 2 : 26 - 31
  • [2] Adaptive classification of two-dimensional gel electrophoretic spot patterns by neural networks and cluster analysis
    Vohradsky, J.
    Electrophoresis, 18 (15):
  • [3] Adaptive classification of two-dimensional gel electrophoretic spot patterns by neural networks and cluster analysis
    Vohradsky, J
    ELECTROPHORESIS, 1997, 18 (15) : 2749 - 2754
  • [4] Gait patterns classification based on cluster and bicluster analysis
    Pauk, J.
    Minta-Bielecka, K.
    BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2016, 36 (02) : 391 - 396
  • [5] ANALYSIS OF ELECTROPHORETIC PATTERNS
    HOXTER, G
    WAJCHENBERG, BL
    MUNGIOLI, R
    NATURE, 1957, 179 (4556) : 423 - 424
  • [6] CLASSIFICATION OF FAMILIAR MOTOR-ACTIVITY PATTERNS USING A CLUSTER METHOD AND SPECTRAL-ANALYSIS
    CAMBRAS, T
    DIEZNOGUERA, A
    RIBOT, M
    JOURNAL OF INTERDISCIPLINARY CYCLE RESEARCH, 1988, 19 (01): : 17 - 22
  • [7] Quantitative Classification of Spring Discharge Patterns: A Cluster Analysis Approach
    Seelig, Magdalena
    Seelig, Simon
    Vremec, Matevz
    Wagner, Thomas
    Brielmann, Heike
    Eybl, Jutta
    Winkler, Gerfried
    HYDROLOGICAL PROCESSES, 2024, 38 (12)
  • [8] A new method of cluster analysis for numerical classification of climate
    C. S. Yao
    Theoretical and Applied Climatology, 1997, 57 : 111 - 118
  • [9] Classification of Color Matching Functions with the Method of Cluster Analysis
    Huang Min
    Guo Chun-li
    He Rui-li
    Xi Yong-hui
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40 (02) : 454 - 460
  • [10] CLASSIFICATION OF SOYBEAN GENOTYPES BY THE CLUSTER-ANALYSIS METHOD
    SICHKAR, VI
    LUGOVOI, AP
    GRIGORYAN, EM
    TSITOLOGIYA I GENETIKA, 1987, 21 (01): : 36 - 41