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Analysis of the Gene Expression Data with 4ft-Miner

This study presents an analysis of gene expression data utilizing the 4ft-Miner tool, developed within the LISp-Miner academic system. It outlines the GUHA method for exploratory data analysis based on association rules, detailing the mining process, the use of 4ft-quantifiers, and essential results derived from the gene expression matrix of size 74x822. The study highlights potential synexpression groups identified from the data, offering important insights into gene relationships and suggesting the need for biological consultation on the significance of the findings.

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Analysis of the Gene Expression Data with 4ft-Miner

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  1. Analysis of the Gene Expression Data with 4ft-Miner Emilia Ylirinne Tampere University of Technology Finland 07.10.2005

  2. Outline • GUHA method in brief • 4ft-quantifiers and 4ft-Miner • Data Mining process • Results • Conclusions

  3. GUHA Method • General Unary Hypotheses Automaton • Introduced in 1960´s by Hájek • Exploratory data analysis based on association rules:    Boolean attributes  and  are associated in the sense of 4ft-quantifier .

  4. GUHA Method • Also conditional association rules   /    • Four fold table corresponding to

  5. Examples of 4ft-quantifiers Founded implication (FUI) =>p, Base, where 0<p≤1 and Base>0 satisfies condition: a/(a+b)≥p and a≥Base

  6. Examples of 4ft-quantifiers Double founded implication (DFUI) <=>p, Base, where 0<p≤1 and Base>0 satisfies condition: a/(a+b+c)≥p and a≥Base

  7. 4ft-Miner • A part of academic system LISp-Miner • http://lispminer.vse.cz/ • Mines for both association and conditional association rules

  8. Data Mining • The small dataset: 74 x 822 gene expression matrix was used • We tried to find potential synexpression groups from data set • Preprocessing based on work of Becquet et al (2002) • With mid-range based approach we got matrix with boolean values 0 and 1

  9. Data Mining Tasks

  10. Results • Example of Task 1 - AAGACAGTGG <=>85%,11 AAGGAGATGG

  11. Results • Example of task 2 GGCAAGAAGA TCACAAGCAA TGTGCTAAAT TGTGTTGAGA <=>100%,10 GCTTTTAAGG / TACAAGAGGA

  12. Conclusions • This study was very preliminary, but there are advantages, which 4ft-Miner can offer • LISp-Miner contains 15 quantifiers • We found numerous results • Even pure equivalencies can be found with conditional association rules • A biologist should be consulted of significance of these results

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