Fuzzy clustering with biological knowledge for gene selection

Ghosh, Sampreeti; Dattagupta, Rana

doi:10.1016/j.asoc.2013.11.007

Cited by 19 publications

(9 citation statements)

References 32 publications

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“…The formula (11) shows that i v is still belongs to the input space, but because of the addition of the weighted coefficient of ( , ) j i K x v ( especially the gaussian kernel) , making it to noise and outliers with different weights, this greatly reduces the influence of noise and outliers on the clustering result.…”

Section: Fuzzy Kernel Clustering Algorithmmentioning

confidence: 99%

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Adaptive Fuzzy Kernel Clustering Algorithm

Wang¹

2015

IJFLS

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Fuzzy clustering algorithm can not obtain good clustering effect when the sample characteristic is not obvious and need to determine the number of clusters firstly. For thi0s reason, this paper proposes an adaptive fuzzy kernel clustering algorithm. The algorithm firstly use the adaptive function of clustering number to calculate the optimal clustering number, then the samples of input space is mapped to highdimensional feature space using gaussian kernel and clustering in the feature space. The Matlab simulation results confirmed that the algorithm's performance has greatly improvement than classical clustering algorithm and has faster convergence speed and more accurate clustering results.

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Section: Fuzzy Kernel Clustering Algorithmmentioning

confidence: 99%

“…STEP 4. According to the formula(11) used the current clustering center and the membership matrix get from step 3 to update the clustering center.STEP 5. Judgement and termination: If…”

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confidence: 99%

Adaptive Fuzzy Kernel Clustering Algorithm

Wang¹

2015

IJFLS

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“…Clustering has proved to be a useful unsupervised learning technique for gene expression data analysis [ 12 ]. The goal is to find a partition of genetics elements represented in the microarray into k distinct groups, where k is the number of clusters which may or may not be known in advance.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies

2018

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BackgroundBiologists aim to understand the genetic background of diseases, metabolic disorders or any other genetic condition. Microarrays are one of the main high-throughput technologies for collecting information about the behaviour of genetic information on different conditions. In order to analyse this data, clustering arises as one of the main techniques used, and it aims at finding groups of genes that have some criterion in common, like similar expression profile. However, the problem of finding groups is normally multi dimensional, making necessary to approach the clustering as a multi-objective problem where various cluster validity indexes are simultaneously optimised. They are usually based on criteria like compactness and separation, which may not be sufficient since they can not guarantee the generation of clusters that have both similar expression patterns and biological coherence.MethodWe propose a Multi-Objective Clustering algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) to find clusters of genes with high levels of co-expression, biological coherence, and also good compactness and separation. Cluster quality indexes are used to optimise simultaneously gene relationships at expression level and biological functionality. Our proposal also includes intensification and diversification strategies to improve the search process.ResultsThe effectiveness of the proposed algorithm is demonstrated on four publicly available datasets. Comparative studies of the use of different objective functions and other widely used microarray clustering techniques are reported. Statistical, visual and biological significance tests are carried out to show the superiority of the proposed algorithm.ConclusionsIntegrating a-priori biological knowledge into a multi-objective approach and using intensification and diversification strategies allow the proposed algorithm to find solutions with higher quality than other microarray clustering techniques available in the literature in terms of co-expression, biological coherence, compactness and separation.

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“…In computational biology, clustering is a useful technique for gene expression data as it groups similar objects together and allows biologist to identify potential relationships between genes [1]. Unsupervised clustering methods have been applied to gene expression data analysis, and the unsupervised ensemble approaches improve accuracy and reliability of clustering results [2].…”

Section: Introductionmentioning

confidence: 99%

An effective fuzzy kernel clustering analysis approach for gene expression data

Sun

Yin

2015

BME

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Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.

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Fuzzy clustering with biological knowledge for gene selection

Cited by 19 publications

References 32 publications

Adaptive Fuzzy Kernel Clustering Algorithm

Adaptive Fuzzy Kernel Clustering Algorithm

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies

An effective fuzzy kernel clustering analysis approach for gene expression data

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