Bioinformatics and Data Mining Studies in Oral Genomics and Proteomics: New Trends and Challenges

Luca Giacomelli*, Ugo Covani
Tirrenian Stomatologic Institute, Via Aurelia 335, Lido di Camaiore (Lucca), Italy

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© Giacomelli and Covani; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Tirrenian Stomatologic Institute, Via Aurelia 335, Lido di Camaiore (Lucca), Italy; Tel: +39 02 87 28 00 09; Fax: +39 02 56 93 314; E-mail:


Genomics and proteomics have promised to change the practice of dentistry and oral pathology, allowing the identification and the characterization of risk factors and therapeutic targets at a molecular level. However, mass-scale molecular genomics and proteomics suffer from some pitfalls: gene/protein expression are significant only if inserted in a detailed network of molecular pathways and gene/gene, gene/protein and protein/protein interactions.

The proper analysis of these complex pictures requires the contribution of theoretical disciplines, like bioinformatics and data mining. In particular, data-mining of existing information could become a strong starting point to formulate new targeted hypotheses and to plan ad hoc experimentation.

In this review, advantages and disadvantages of the above-mentioned disciplines and their potential in oral pathology are discussed. The leader gene approach is a new data mining algorithm, recently applied to some oral diseases and their correlation with systemic conditions. The preliminary results of the application of the leader gene approach to the correlation between periodontitis and heart ischemia at a molecular level are presented for the first time.

Keywords: Bioinformatics, data mining, gene interaction, genomics, heart ischemia, oral diseases, periodontitis, proteomics.