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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Rough-fuzzy clustering for grouping functionally similar genes from microarray data.

Pradipta Maji1, Sushmita Paul

  • 1Machine Intelligence Unit, Indian Statistical Institute, 203 BT Road, Kolkata 700108, West Bengal, India. pmaji@isical.ac.in

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|August 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a robust rough-fuzzy c-means algorithm for gene expression data clustering. It effectively identifies coexpressed gene clusters by integrating rough and fuzzy set theories to handle uncertainty and noise in biological data.

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Area of Science:

  • Functional Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Gene expression data clustering is crucial for understanding gene functional relationships in biological processes.
  • Identifying coexpressed gene groups is a fundamental challenge in gene clustering.
  • Existing methods struggle with uncertainty, vagueness, and noise in gene expression data.

Purpose of the Study:

  • To propose a novel gene clustering algorithm, robust rough-fuzzy c-means, for improved functional genomics analysis.
  • To leverage the strengths of rough sets and fuzzy sets for robust gene expression data clustering.
  • To develop an efficient method for selecting initial prototypes to enhance clustering accuracy and convergence.

Main Methods:

  • Integration of rough set theory (lower and upper approximations) to manage uncertainty and vagueness in cluster definitions.
  • Incorporation of fuzzy set theory (probabilistic and possibilistic memberships) to handle overlapping partitions in noisy environments.
  • Development of a robust rough-fuzzy c-means algorithm with novel concepts like possibilistic lower bound and probabilistic boundary for efficient cluster selection and prototype initialization.

Main Results:

  • The proposed robust rough-fuzzy c-means algorithm demonstrates effective identification of coexpressed gene clusters.
  • The algorithm shows improved performance in handling uncertainty, vagueness, and noise compared to existing methods.
  • Quantitative and qualitative evaluations on 14 yeast microarray datasets validate the algorithm's effectiveness and convergence to near-optimal solutions.

Conclusions:

  • The robust rough-fuzzy c-means algorithm offers a powerful and efficient tool for gene expression data clustering in functional genomics.
  • The integration of rough and fuzzy set theories provides a robust framework for analyzing complex and noisy biological data.
  • The proposed method facilitates the discovery of biologically relevant coexpressed gene clusters, advancing our understanding of gene functions.