Clustering Algorithms: Their Application to Gene Expression Data
Jelili Oyelade,Itunuoluwa Isewon,Funke Oladipupo,Olufemi Aromolaran,Efosa Uwoghiren,Faridah Ameh,Moses Achas,Ezekiel Adebiyi +7 more
TLDR
This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.Abstract:
Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.read more
Citations
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Journal ArticleDOI
COIN: Correlation Index-Based Similarity Measure for Clustering Categorical Data
N. Sowmiya,Namrata S Gupta,Elango Natarajan,B. Valarmathi,Irraivan Elamvazuthi,S. Parasuraman,Chun Ang Kit,Lidio Inacio Freitas,Ezra Morris Abraham Gnanamuthu +8 more
TL;DR: The proposed COIN algorithm is compared with five existing categorical clustering algorithms such as Mean Gain Ratio (MGR), Min–Min-Roughness (MMR), COOLCAT, K-AN MI, and G-ANMI and reports that COIN outperforms other algorithms.
Journal ArticleDOI
A review of clustering algorithms for determination of cancer signatures
TL;DR: This review examines the various clustering algorithms that could be applied to the gene expression data, aiming to identify the signature genes of biological diseases, which is one the most significant applications of clustering techniques.
Book ChapterDOI
Clustering Analysis Indicates Genes Involved in Progesterone-Induced Oxidative Stress in Pancreatic Beta Cells: Insights to Understanding Gestational Diabetes
TL;DR: In this paper , an extension of this analysis by including internal indices and applying it in a study case to investigate gestational diabetes through experiments on microarray data of pancreatic beta cells submitted to supra-pharmacological doses of progesterone.
Journal ArticleDOI
Single nucleotide polymorphisms of enamel formation genes and early childhood caries - systematic review, gene-based, gene cluster and meta-analysis
Aruna Sharma,Sneha S. Patil,Murugan Satta Muthu,Vettriselvi Venkatesan,Richard Kirubakaran,Sivakumar Nuvvula,S. Arockiam +6 more
TL;DR: In this paper , a systematic review aimed to analyze associations between single-nucleotide polymorphisms of enamel formation genes and ECC, and revealed significant association between six variants of AMBN, four variants of KLK4, two variants of MMP20, and a single variant of each of the MMP9 and MMP13 genes.
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Scikit-learn: Machine Learning in Python
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