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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The Self-Organizing Map
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Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Monika A Myszczynska,Poojitha N. Ojamies,Alix M. B. Lacoste,Daniel Neil,Amir Saffari,Richard J. Mead,Guillaume M. Hautbergue,Joanna D. Holbrook,Laura Ferraiuolo +8 more
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A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects
Ezugwu E. Absalom,Abiodun Motunrayo Ikotun,Olaide Nathaniel Oyelade,Laith Abualigah,Jeffrey O. Agushaka,Christopher Ifeanyi Eke,Andronicus Ayobami Akinyelu +6 more
TL;DR: Clustering is an essential tool in data mining research and applications as discussed by the authors and it is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning.
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Deep learning-based clustering approaches for bioinformatics
Md. Rezaul Karim,Oya Beyan,Oya Beyan,Achille Zappa,Ivan G. Costa,Dietrich Rebholz-Schuhmann,Michael Cochez,Stefan Decker,Stefan Decker +8 more
TL;DR: In this article, the authors present a review of state-of-the-art DL-based approaches for clustering analysis that are based on representation learning, which they hope to be useful for bioinformatics research.
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Single-cell transcriptomic evidence for dense intracortical neuropeptide networks
Stephen J. Smith,Uygar Sümbül,Lucas T. Graybuck,Forrest Collman,Sharmishtaa Seshamani,Rohan Gala,Olga Gliko,Leila Elabbady,Jeremy A. Miller,Trygve E. Bakken,Jean Rossier,Zizhen Yao,Ed Lein,Hongkui Zeng,Bosiljka Tasic,Michael Hawrylycz +15 more
TL;DR: Here, neuron-type-specific patterns of NP gene expression are used to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.
References
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