scholarly journals Evolutionary Clustering Algorithm with Knowledge-Based Evaluation for Fuzzy Cluster Analysis of Gene Expression Profiles

2005 ◽  
pp. 640-644
Author(s):  
Han-Saem Park ◽  
Sung-Bae Cho
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Clustering Algorithm ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Evolutionary Clustering ◽  
Knowledge Based

Unique gene expression profiles of human macrophages and dendritic cells to phylogenetically distinct parasites

Blood ◽  
2003 ◽  
Vol 102 (2) ◽  
pp. 672-681 ◽  
Author(s):  
Damien Chaussabel ◽  
Roshanak Tolouei Semnani ◽  
Mary Ann McDowell ◽  
David Sacks ◽  
Alan Sher ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dendritic Cells ◽  
Leishmania Donovani ◽  
Clustering Algorithm ◽  
Leishmania Major ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Infectious Agent ◽  
Experimental Conditions ◽  
Characteristic Functional

AbstractMonocyte-derived dendritic cells (DCs) and macrophages (Mϕs) generated in vitro from the same individual blood donors were exposed to 5 different pathogens, and gene expression profiles were assessed by microarray analysis. Responses to Mycobacterium tuberculosis and to phylogenetically distinct protozoan (Leishmania major, Leishmania donovani, Toxoplasma gondii) and helminth (Brugia malayi) parasites were examined, each of which produces chronic infections in humans yet vary considerably in the nature of the immune responses they trigger. In the absence of microbial stimulation, DCs and Mϕs constitutively expressed approximately 4000 genes, 96% of which were shared between the 2 cell types. In contrast, the genes altered transcriptionally in DCs and Mϕs following pathogen exposure were largely cell specific. Profiling of the gene expression data led to the identification of sets of tightly coregulated genes across all experimental conditions tested. A newly devised literature-based clustering algorithm enabled the identification of functionally and transcriptionally homogenous groups of genes. A comparison of the responses induced by the individual pathogens by means of this strategy revealed major differences in the functionally related gene profiles associated with each infectious agent. Although the intracellular pathogens induced responses clearly distinct from the extracellular B malayi, they each displayed a unique pattern of gene expression that would not necessarily be predicted on the basis of their phylogenetic relationship. The association of characteristic functional clusters with each infectious agent is consistent with the concept that antigen-presenting cells have prewired signaling patterns for use in the response to different pathogens.

scholarly journals Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

2019 ◽  
Vol 9 (4) ◽  
pp. 2760
Author(s):  
K. Varada Rajkumar ◽  
Adimulam Yesubabu ◽  
K. Subrahmanyam
Keyword(s):  
Cluster Analysis ◽  
Fuzzy Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Fuzzy Cluster ◽  
Fuzzy Cluster Analysis ◽  
Fuzzy C Means ◽  
A Value ◽  
Data Points ◽  
Partition Clustering

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value &lt; 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter <em>m</em> was evaluated to determine the distribution of membership values with variation in <em>m</em> from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.

scholarly journals Analysis of Gene Expression Profiles of Soft Tissue Sarcoma Using a Combination of Knowledge-Based Filtering with Integration of Multiple Statistics

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106801 ◽  
Author(s):  
Anna Takahashi ◽  
Robert Nakayama ◽  
Nanako Ishibashi ◽  
Ayano Doi ◽  
Risa Ichinohe ◽  
...  
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Knowledge Based

scholarly journals The Classification ofSiniDecoction Pattern in Traditional Chinese Medicine by Gene Expression Profiling

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Hung-Tsu Cheng ◽  
Chaang-Ray Chen ◽  
Chia-Yang Li ◽  
Chao-Ying Huang ◽  
Wun-Yi Shu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clustering Algorithm ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
City Hospital ◽  
Test Results ◽  
Peripheral Blood Mononuclear

We investigated the syndromes of theSinidecoction pattern (SDP), a common ZHENG in traditional Chinese medicine (TCM). The syndromes of SDP were correlated with various severeYang deficiencyrelated symptoms. To obtain a common profile for SDP, we distributed questionnaires to 300 senior clinical TCM practitioners. According to the survey, we concluded 2 sets of symptoms for SDP: (1) pulse feels deep or faint and (2) reversal cold of the extremities. Twenty-four individuals from Taipei City Hospital, Linsen Chinese Medicine Branch, Taiwan, were recruited. We extracted the total mRNA of peripheral blood mononuclear cells from the 24 individuals for microarray experiments. Twelve individuals (including 6 SDP patients and 6 non-SDP individuals) were used as the training set to identify biomarkers for distinguishing the SDP and non-SDP groups. The remaining 12 individuals were used as the test set. The test results indicated that the gene expression profiles of the identified biomarkers could effectively distinguish the 2 groups by adopting a hierarchical clustering algorithm. Our results suggest the feasibility of using the identified biomarkers in facilitating the diagnosis of TCM ZHENGs. Furthermore, the gene expression profiles of biomarker genes could provide a molecular explanation corresponding to the ZHENG of TCM.

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