Aggregation of multi-objective fuzzy symmetry-based clustering techniques for improving gene and cancer classification

Saha, Sriparna; Das, Ranjita

doi:10.1007/s00500-017-2865-3

Cited by 7 publications

(3 citation statements)

References 62 publications

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“…Once the missing values are imputed, machine learning techniques, including clustering, can be applied to analyze the complete data sets. In recent works, multi-objective optimization (Deb et al 2002) had been employed to deal with the gene clustering problem and exhibited better performance than single-objective clustering methods (Bandyopadhyay et al 2007;Faceli et al 2009;Giri and Sara 2020;Maulik et al 2009;Mukhopadhyay et al 2013;Sara et al 2013;Sara et al 2018). A multi-objective optimization problem can be formulated as (Deb et al 2002):…”

Section: Related Workmentioning

confidence: 99%

“…Considering the adaptive selection of cluster validity indices, Mukhopadhyay et al (2013) proposed an interactive multi-objective clustering approach that simultaneously found the patterns of gene expression data and the best set of validity measures. By using a link-based clustering ensemble technique, Sara et al (2018) proposed two multi-objective symmetry-based clustering methods. To involve GO-based biological knowledge during the clustering process, Giri et al (2020) proposed a multi-view multi-objective clustering approach, which treated the GO-based and expression-based similarities of genes as complementary views.…”

Section: Related Workmentioning

confidence: 99%

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A Joint Optimization Framework Integrated with Biological Knowledge for Clustering Incomplete Gene Expression Data

Li¹,

Gu²,

Chang³

et al. 2021

Preprint

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Clustering algorithms have been successfully applied to identify co-expressed gene groups from gene expression data. Missing values often occur in gene expression data, which presents a challenge for gene clustering. When partitioning incomplete gene expression data into co-expressed gene groups, missing value imputation and clustering are generally performed as two separate processes. These two-stage methods are likely to result in unsuitable imputation values for clustering task and unsatisfying clustering performance. This paper proposes a multi-objective joint optimization framework for clustering incomplete gene expression data that addresses this problem. The proposed framework can impute the missing expression values under the guidance of clustering, and therefore realize the synergistic improvement of imputation and clustering. In addition, gene expression similarity and gene semantic similarity extracted from the Gene Ontology are combined, as the form of functional neighbor interval for each missing expression value, to provide reasonable constraints for the joint optimization framework. Experiments on several benchmark data sets confirm the effectiveness of the proposed framework.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Joint Optimization Framework Integrated with Biological Knowledge for Clustering Incomplete Gene Expression Data

Li¹,

Gu²,

Chang³

et al. 2021

Preprint

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“…S. Saha, et al [20] established ensemble based clustering namely Multi-Objective (MO) fuzzy technique for enhancing the performance of cancer gene classification. The few processes are merged with the ensemble based framework (i) To detect the overlapped clusters, fuzzy logic is used (ii) In order to identify the various shape of the clusters and calculated the distance between the clusters by symmetry based distance measure.…”

Section: Analysis Of Cancer Gene Clustering Techniquesmentioning

confidence: 99%

Review on Clustering Cancer Genes

Prabhuraj¹,

Shastry²,

Patil³

2019

IJCET

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Present studies, development of genomic technologies are highly concentrated on galactic scale gene data. In Bioinformatics community, the sizable volume of gene data investigation and distinguishing the behavior of genes in antithetical conditions are the intriguing task. This cognitive factor can be deal by the clustering technique, its groups the similarity patterns at various features. Moreover, gene expression data indicates the contrastive levels of gene behaviors in various tissue cells and it does provide the feature information effectively. This gene clustering investigation is precise and accommodating in cancer uncovering because of its easiness to detect the cancerous and non-cancerous genes. The precautionary measures cancer diagnostic is precise crucial for cancer prevention and treatment. The existing cancer gene clustering techniques includes several limitations such as time complexities in training and testing samples, maximum redundant features and high dimensional data. These issues are severely influences the data clustering accuracy. This paper focuses on survey of various clustering techniques of cancer gene clustering with respect to cancer gene benchmark datasets. Furthermore, review of existing cancer gene clustering technique describes the advantages and limitations comprehensively.

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