A methodology is proposed to automatically detect significant symbol associations in genomic databases. A new statistical test assesses the significance of a group of symbols when found in several genesets of a given database. To each pair of symbols, a p-value depending on the frequency of the two symbols and on the number of joint occurrences, is associated. All pairs with p-values below a certain threshold define a graph structure on the set of symbols. The cliques of that graph are significant symbol associations, linked to a set of genesets where they can be found. The method can be applied to any database, and is illustrated on the MSigDB C2 database. Many of the symbol associations detected in C2 or in non-specific selections correspond to already known interactions. On more specific selections of C2, many previously unknown symbol associations have been detected. These associations unveal new candidates for gene or protein interactions, needing further investigation for biological evidence.
| Published in | International Journal of Genetics and Genomics (Volume 2, Issue 6) |
| DOI | 10.11648/j.ijgg.20140206.11 |
| Page(s) | 97-104 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Genomic Databases, Protein-Protein Interaction, Frequent Itemset Searching, P-Value Graph
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APA Style
Bernard Ycart, Frederic Pont, Jean-Jacques Fournie. (2014). Statistical Data Mining for Symbol Associations in Genomic Databases. International Journal of Genetics and Genomics, 2(6), 97-104. https://doi.org/10.11648/j.ijgg.20140206.11
ACS Style
Bernard Ycart; Frederic Pont; Jean-Jacques Fournie. Statistical Data Mining for Symbol Associations in Genomic Databases. Int. J. Genet. Genomics 2014, 2(6), 97-104. doi: 10.11648/j.ijgg.20140206.11
AMA Style
Bernard Ycart, Frederic Pont, Jean-Jacques Fournie. Statistical Data Mining for Symbol Associations in Genomic Databases. Int J Genet Genomics. 2014;2(6):97-104. doi: 10.11648/j.ijgg.20140206.11
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Download@article{10.11648/j.ijgg.20140206.11,
author = {Bernard Ycart and Frederic Pont and Jean-Jacques Fournie},
title = {Statistical Data Mining for Symbol Associations in Genomic Databases},
journal = {International Journal of Genetics and Genomics},
volume = {2},
number = {6},
pages = {97-104},
doi = {10.11648/j.ijgg.20140206.11},
url = {https://doi.org/10.11648/j.ijgg.20140206.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijgg.20140206.11},
abstract = {A methodology is proposed to automatically detect significant symbol associations in genomic databases. A new statistical test assesses the significance of a group of symbols when found in several genesets of a given database. To each pair of symbols, a p-value depending on the frequency of the two symbols and on the number of joint occurrences, is associated. All pairs with p-values below a certain threshold define a graph structure on the set of symbols. The cliques of that graph are significant symbol associations, linked to a set of genesets where they can be found. The method can be applied to any database, and is illustrated on the MSigDB C2 database. Many of the symbol associations detected in C2 or in non-specific selections correspond to already known interactions. On more specific selections of C2, many previously unknown symbol associations have been detected. These associations unveal new candidates for gene or protein interactions, needing further investigation for biological evidence.},
year = {2014}
}
TY - JOUR T1 - Statistical Data Mining for Symbol Associations in Genomic Databases AU - Bernard Ycart AU - Frederic Pont AU - Jean-Jacques Fournie Y1 - 2014/12/02 PY - 2014 N1 - https://doi.org/10.11648/j.ijgg.20140206.11 DO - 10.11648/j.ijgg.20140206.11 T2 - International Journal of Genetics and Genomics JF - International Journal of Genetics and Genomics JO - International Journal of Genetics and Genomics SP - 97 EP - 104 PB - Science Publishing Group SN - 2376-7359 UR - https://doi.org/10.11648/j.ijgg.20140206.11 AB - A methodology is proposed to automatically detect significant symbol associations in genomic databases. A new statistical test assesses the significance of a group of symbols when found in several genesets of a given database. To each pair of symbols, a p-value depending on the frequency of the two symbols and on the number of joint occurrences, is associated. All pairs with p-values below a certain threshold define a graph structure on the set of symbols. The cliques of that graph are significant symbol associations, linked to a set of genesets where they can be found. The method can be applied to any database, and is illustrated on the MSigDB C2 database. Many of the symbol associations detected in C2 or in non-specific selections correspond to already known interactions. On more specific selections of C2, many previously unknown symbol associations have been detected. These associations unveal new candidates for gene or protein interactions, needing further investigation for biological evidence. VL - 2 IS - 6 ER -
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