Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data

  title={Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data},
  author={Balamurugan Rengeswaran and A. Natarajan and K. Premalatha},
  journal={Applied Artificial Intelligence},
  pages={353 - 381}
  • Balamurugan Rengeswaran, A. Natarajan, K. Premalatha
  • Published 2015
  • Computer Science
  • Applied Artificial Intelligence
  • DNA microarray gene expression data analysis has provided new insights into gene function, disease pathophysiology, disease classification, and drug development. Biclustering in gene expression data is a subset of the genes demonstrating consistent patterns over a subset of the conditions. The proposed work finds the significant biclusters in large expression data using a novel optimization technique called stellar-mass black hole optimization (SBO). This optimization algorithm is inspired from… CONTINUE READING


    Publications referenced by this paper.
    Biclustering algorithms for biological data analysis: a survey
    • 1,979
    • PDF
    Genomic expression programs in the response of yeast cells to environmental changes.
    • 4,488
    • PDF
    Biclustering of Expression Data
    • 2,141
    • Highly Influential
    • PDF
    Discovering statistically significant biclusters in gene expression data
    • 912
    • PDF
    Cuckoo Search via Lévy flights
    • 3,772
    • PDF
    A genome-wide transcriptional analysis of the mitotic cell cycle.
    • 2,235
    • PDF
    Genomics, gene expression and DNA arrays
    • 1,989
    • PDF
    A comparative study of Artificial Bee Colony algorithm
    • 2,359
    • PDF
    Multi-objective evolutionary biclustering of gene expression data
    • 226
    Discovering local structure in gene expression data: the order-preserving submatrix problem
    • 550
    • Highly Influential
    • PDF