Inferential Clustering Approach for Microarray Experiments with Replicated Measurements

2009 ◽  
Vol 6 (4) ◽  
pp. 594-604 ◽  
Author(s):  
M. Salicru ◽  
S. Vives ◽  
Tian Zheng
Keyword(s):  
Microarray Experiments ◽  
Replicated Measurements ◽  
Clustering Approach

BIOLOGICAL ANALYSIS OF MICROARRAY DATA USING ORTHOGONAL FORWARD SELECTION WITH A CLUSTERING APPROACH

2015 ◽  
Vol 23 (02) ◽  
pp. 275-288 ◽  
Author(s):  
WONG SOU KAH ◽  
KOHBALAN MOORTHY ◽  
MOHD SABERI MOHAMAD ◽  
SHAHREEN KASIM ◽  
SAFAAI DERIS ◽  
...  
Keyword(s):  
Gene Selection ◽  
Computation Time ◽  
Small Subset ◽  
Biological Processes ◽  
Strongly Correlated ◽  
Forward Selection ◽  
Combined Method ◽  
Detection Capability ◽  
Microarray Experiments ◽  
Clustering Approach

DNA microarray technology allows researchers to monitor the expression level of thousands of genes under various conditions in microarray experiments. However, high-dimensional data in microarray is a major challenge as the irrelevant genes often reduce the detection capability and increase the computation time. Many learning algorithms are not specifically developed to deal with the noisy genes, thus, incorporating them with gene selection techniques has become a necessity. In this paper, we propose a combined method of Gram–Schmidt orthogonal forward selection (OFS) and FunCluster to search for putatively co-regulated biological processes that share the co-expressed genes. There were two datasets used in this research: human white adipose tissue and human skeletal muscle. This study aimed to find a small subset of strongly correlated genes from the raw datasets to maximize the detection capability of cluster analysis. This method was found able to detect the clusters of biological categories that were overlooked in the previous research. Some clusters represented minor functions of the datasets and indicated more specific biological processes. Further, the computation time for both datasets was reduced using this proposed method, as the Gram–Schmidt OFS significantly reduced the dimensionality of the datasets.

Optimal Model-Free Approach Based on MDL and CHL for Active Brain Identification in fMRI Data Analysis

2020 ◽  
Vol 16 ◽  
Author(s):  
Hussain A. Jaber ◽  
Ilyas Çankaya ◽  
Hadeel K. Aljobouri ◽  
Orhan M. Koçak ◽  
Oktay Algin
Keyword(s):  
Data Analysis ◽  
Time Course ◽  
Extreme Values ◽  
Statistical Parametric Mapping ◽  
Fmri Data ◽  
Cluster Center ◽  
Jaccard Coefficient ◽  
Neural Gas ◽  
Model Free ◽  
Clustering Approach

Background: Cluster analysis is a robust tool for exploring the underlining structures in data and grouping them with similar objects. In the researches of Functional Magnetic Resonance Imaging (fMRI), clustering approaches attempt to classify voxels depending on their time-course signals into a similar hemodynamic response over time. Objective: In this work, a novel unsupervised learning approach is proposed that relies on using Enhanced Neural Gas (ENG) algorithm in fMRI data for comparison with Neural Gas (NG) method, which has yet to be utilized for that aim. The ENG algorithm depends on the network structure of the NG and concentrates on an efficacious prototype-based clustering approach. Methods: The comparison outcomes on real auditory fMRI data show that ENG outperforms the NG and statistical parametric mapping (SPM) methods due to its insensitivity to the ordering of input data sequence, various initializations for selecting a set of neurons, and the existence of extreme values (outliers). The findings also prove its capability to discover the exact and real values of a cluster number effectively. Results: Four validation indices are applied to evaluate the performance of the proposed ENG method with fMRI and compare it with a clustering approach (NG algorithm) and model-based data analysis (SPM). These validation indices include the Jaccard Coefficient (JC), Receiver Operating Characteristic (ROC), Minimum Description Length (MDL) value, and Minimum Square Error (MSE). Conclusion: The ENG technique can tackle all shortcomings of NG application with fMRI data, identify the active area of the human brain effectively, and determine the locations of the cluster center based on the MDL value during the process of network learning.

scholarly journals Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2125
Author(s):  
Bardia Yousefi ◽  
Clemente Ibarra-Castanedo ◽  
Martin Chamberland ◽  
Xavier P. V. Maldague ◽  
Georges Beaudoin
Keyword(s):  
Matched Filter ◽  
Principal Component ◽  
Hyperspectral Data ◽  
Clustering Methods ◽  
Spectral Angle Mapper ◽  
Long Wave ◽  
Clustering Approach ◽  
Spectral Comparison ◽  
Computational Simplicity ◽  
Mineral Identification

Clustering methods unequivocally show considerable influence on many recent algorithms and play an important role in hyperspectral data analysis. Here, we challenge the clustering for mineral identification using two different strategies in hyperspectral long wave infrared (LWIR, 7.7–11.8 μm). For that, we compare two algorithms to perform the mineral identification in a unique dataset. The first algorithm uses spectral comparison techniques for all the pixel-spectra and creates RGB false color composites (FCC). Then, a color based clustering is used to group the regions (called FCC-clustering). The second algorithm clusters all the pixel-spectra to directly group the spectra. Then, the first rank of non-negative matrix factorization (NMF) extracts the representative of each cluster and compares results with the spectral library of JPL/NASA. These techniques give the comparison values as features which convert into RGB-FCC as the results (called clustering rank1-NMF). We applied K-means as clustering approach, which can be modified in any other similar clustering approach. The results of the clustering-rank1-NMF algorithm indicate significant computational efficiency (more than 20 times faster than the previous approach) and promising performance for mineral identification having up to 75.8% and 84.8% average accuracies for FCC-clustering and clustering-rank1 NMF algorithms (using spectral angle mapper (SAM)), respectively. Furthermore, several spectral comparison techniques are used also such as adaptive matched subspace detector (AMSD), orthogonal subspace projection (OSP) algorithm, principal component analysis (PCA), local matched filter (PLMF), SAM, and normalized cross correlation (NCC) for both algorithms and most of them show a similar range in accuracy. However, SAM and NCC are preferred due to their computational simplicity. Our algorithms strive to identify eleven different mineral grains (biotite, diopside, epidote, goethite, kyanite, scheelite, smithsonite, tourmaline, pyrope, olivine, and quartz).

An Overlapping Clustering Approach for Precision, Diversity and Novelty-Aware Recommendations

2021 ◽  
pp. 114917
Author(s):  
Chems Eddine Berbague ◽  
Nour El-islam Karabadji ◽  
Hassina Seridi ◽  
Panagiotis Symeonidis ◽  
Yannis Manolopoulos ◽  
...  
Keyword(s):  
Overlapping Clustering ◽  
Clustering Approach
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