Growing Hierarchical Self-Organizing Map (GHSOM) for Mining Gene Expression Data

No abstract is available for this paper.

• Publication year

  2015

• Venue

  International Journal of Computer Applications

• Publication date

  2015-01-16

• Fields of study

  Biology, Computer Science

• Identifiers
  DOI 10.5120/19160-0603
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Using K-Means Clustering Technique to Study of Breast Cancer

  2014cited by this paper
• Cluster center genes as candidate biomarkers for the classification of Leukemia

  2014cited by this paper
• Mining Gene Expression Data Focusing Cancer Therapeutics: A Digest

  2014cited by this paper
• Mining breast cancer genetic data

  2013cited by this paper
• An Integrative Genomics Approach to Biomarker Discovery in Breast Cancer

  2011cited by this paper
• Microarray gene selection using self-organizing map

  2007cited by this paper
• Interactive Gene Clustering—A Case Study of Breast Cancer Microarray Data

  2006cited by this paper
• 21.10 n&v 915 MH

  2004cited by this paper
• Molecular classification of cancer: unsupervised self-organizing map analysis of gene expression microarray data.

  2003cited by this paper
• Molecular Classification of Cancer : Unsupervised Self-Organizing Map Analysis of Gene Expression Microarray Data 1

  2003cited by this paper
• Self-organizing map for cluster analysis of a breast cancer database

  2003cited by this paper
• The growing hierarchical self-organizing map

  2000cited by this paper
• Systematic determination of genetic network architecture

  1999cited by this paper
• and as an in

  year unknowncited by this paper

• Using Fuzzy Logic Algorithms and Growing Hierarchical Self-Organizing Maps to Define Efficient Security Inspection Strategies in a Container Terminal

  2020cites this paper
• Energy based Methods for Medical Image Segmentation

  2016cites this paper