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
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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
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Book ChapterDOI
Introduction to Clustering
TL;DR: The commonly used tasks and terminologies in data analysis and the importance of data clustering are defined and a mathematical formulation of the clustering problem is given and the frequently used similarity measures are explained.
Journal ArticleDOI
TRIQ: a new method to evaluate triclusters.
TL;DR: TRIQ appears as a valid measure to represent and summarize the quality of tricluster solutions, and is feasible for evaluation of non biological triclusters, due to the parametrization of each component of TRIQ.
Journal ArticleDOI
Simultaneous feature selection and clustering of micro-array and RNA-sequence gene expression data using multiobjective optimization
TL;DR: A multiobjective optimization solution framework for solving the problem of gene expression data clustering in reduced feature space and the proposed FSC-MOO technique outperforms the existing nine clustering techniques.
Posted ContentDOI
Transcriptomic evidence for dense peptidergic neuromodulation networks in mouse cortex
Stephen J. Smith,Uygar Sümbül,Lucas T. Graybuck,Forrest Collman,Sharmishtaa Seshamani,Rohan Gala,Olga Gliko,Leila Elabbady,Jeremy A. Miller,Trygve E. Bakken,Zizhen Yao,Ed Lein,Hongkui Zeng,Bosiljka Tasic,Michael Hawrylycz +14 more
TL;DR: Results from deep RNA-Seq analysis of 22,439 individual mouse neocortical neurons are analyzed to generate testable predictions regarding dense peptidergic neuromodulatory networks that may play prominent roles in cortical activity homeostasis and memory engram storage.
Proceedings ArticleDOI
Multi-View Gene Clustering using Gene Ontology and Expression-based Similarities
TL;DR: A multi-view multi-objective clustering approach where Euclidean distances between the gene expression values and GO-based multi-factored gene-gene semantic similarity are considered as two complementary views and a consensus partitioning is obtained that satisfies both the aspects or views.
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