Clustering: Unsupervised Learning in Large Biological Data

Belacel, Nabil; Wang, Christa; Cupelovic‐Culf, Miroslava

doi:10.1002/9780470567647.ch5

Cited by 5 publications

(2 citation statements)

References 65 publications

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“…From a machine learning perspective, the search for meaningful clusters is defined as unsupervised learning due to the lack of prior knowledge on the number of clusters and their labels. However, clustering is a widely used exploratory tool for analyzing large datasets and has been applied extensively in numerous biological, genomics, proteomics, and various other omics methodologies [3]. Genomics is one of the most important domains in bioinformatics, whereas the number of sequences available is increasing exponentially [1].…”

Section: Introductionmentioning

confidence: 99%

A Multi-metric Algorithm for Hierarchical Clustering of Same-Length Protein Sequences

Tsarouchis

Kotouza

Psomopoulos

et al. 2018

IFIP Advances in Information and Communication Technology

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The identification of meaningful groups of proteins has always been a major area of interest for structural and functional genomics. Successful protein clustering can lead to significant insight, assisting in both tracing the evolutionary history of the respective molecules as well as in identifying potential functions and interactions of novel sequences. Here we propose a clustering algorithm for same-length sequences, which allows the construction of subset hierarchy and facilitates the identification of the underlying patterns for any given subset. The proposed method utilizes the metrics of sequence identity and amino-acid similarity simultaneously as direct measures. The algorithm was applied on a realworld dataset consisting of clonotypic immunoglobulin (IG) sequences from Chronic lymphocytic leukemia (CLL) patients, showing promising results.

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Section: Introductionmentioning

confidence: 99%

A Multi-metric Algorithm for Hierarchical Clustering of Same-Length Protein Sequences

Tsarouchis

Kotouza

Psomopoulos

et al. 2018

IFIP Advances in Information and Communication Technology

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show abstract

“…The increasingly large amount of data related to DNA, proteins, molecular compounds, gene expressions and other biological sciences, raises the need for advanced analytical tools to support a data-driven scientific discovery. Classification and clustering of high-dimensional data, for example, are very popular techniques for the analysis of large multidimensional biological datasets [ 1 ]. Classification methods are based on a supervised learning approach, where the patterns of the training set belong to pre-defined classes.…”

Section: Introductionmentioning

confidence: 99%

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

et al. 2014

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BackgroundIn many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications.ResultsThis work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds.ConclusionsThe number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

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Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks

Guitouni,

Belacel,

Benabbou

et al. 2024

Sci Rep

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Clustering: Unsupervised Learning in Large Biological Data

Cited by 5 publications

References 65 publications

A Multi-metric Algorithm for Hierarchical Clustering of Same-Length Protein Sequences

A Multi-metric Algorithm for Hierarchical Clustering of Same-Length Protein Sequences

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks

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