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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Clique-based data mining for related genes in a biomedical database.

Tsutomu Matsunaga1, Chikara Yonemori, Etsuji Tomita

  • 1Research and Development Headquarters, NTT DATA Corporation, Tokyo 135-8671, Japan. matsunagat@nttdata.co.jp

BMC Bioinformatics
|July 2, 2009
PubMed
Summary

This study introduces a data mining approach to identify related genes by analyzing biomedical databases. The method extracts gene modules, offering a holistic view for understanding complex genetic mechanisms and diseases.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Genetics

Background:

  • Integrating diverse gene knowledge is crucial for understanding biological phenomena and diseases.
  • A need exists for automated methods to search biomedical databases for related genes, such as those in the same families or pathways.
  • This study addresses the challenge of extracting related genes using densely-connected subgraphs within a biomedical relational graph.

Purpose of the Study:

  • To develop and present a data mining approach for extracting sets of related genes.
  • To model relationships between genes and diseases using a biomedical relational graph.
  • To computationally identify gene modules by enumerating cliques.

Main Methods:

  • Constructed a graph using gene and disease pages from the Online Mendelian Inheritance in Man (OMIM) database.
  • Utilized hyperlink connections as edges in the graph.
  • Employed clique enumeration to computationally identify over 20,000 sets of related genes, termed 'gene modules'.

Main Results:

  • Identified over 20,000 gene modules, encompassing genes within the same families, protein complexes, and signaling pathways.
  • Experimental results with 'metabolic syndrome'-related gene modules demonstrated their utility in providing a coherent, holistic understanding of gene relationships.
  • The extracted gene sets facilitate a comprehensive interpretation of gene interactions.

Conclusions:

  • Presented a novel data mining approach for extracting related genes through clique enumeration.
  • The identified gene sets offer a holistic perspective valuable for deciphering complex disease mechanisms.
  • This method enhances the integration of biomedical knowledge for hypothesis generation.