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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Proceedings ArticleDOI
Cluster Switches in Gene Expression Data
TL;DR: A heuristic framework, CSGI (cluster switching genes identification) for identifying promising genes for thorough analysis, and provides a case study of immune system clusters showing that the approach identifies clusters representing core conserved biological processes, as well as important genes that switch of clusters.
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SGAClust: Semi-supervised Graph Attraction Clustering of gene expression data
Koyel Mandal,Rosy Sarmah +1 more
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Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis
TL;DR: By providing a better treatment of the noise inherent in repeated measurements and taking into account multiple layers of poly(A) site data, PASCCA could be a general tool for clustering and analyzing APA-specific gene expression data.
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EXAMINING USABILITY, ACCEPTABILITY AND ADOPTION OF A SELF-DIRECTED, TECHNOLOGY-BASED INTERVENTION FOR UPPER LIMB REHABILITATION AMONGST STROKE SURVIVORS: A FEASIBILITY STUDY (Preprint)
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Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data
TL;DR: Two clustering-based and three transform-based feature extraction methods, namely, Fuzzy C Means, Softmax Discriminant Algorithm, Hilbert Transform, Fast Fourier Transform, and Discrete Cosine Transform, respectively, are used to select relevant genes further and six classifiers further classify the features as normal and abnormal.
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Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
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A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
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