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Journal of Computer Science & Systems Biology
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Research Article

A Leader Genes Approach-based Tool for Molecular Genomics: From Gene-ranking to Gene-network Systems Biology and Biotargets Predictions

Nicola Luigi Bragazzi1,2 and Claudio Nicolini1,2,3*

1Laboratories of Biophysics and Nanobiotechnology, Department of Experimental Medicine (DIMES), University of Genova, Via Antonio Pastore 3, 16132 Genova, Italy

2Nanoworld Institute, Fondazione EL.B.A. Nicolini, Largo Redaelli 7, Pradalunga, Bergamo, Italy

3Biodesign Institute, Arizona State University, Tempe, USA

*Corresponding Author:
Claudio Nicolini
Laboratories of Biophysics and Nanobiotechnology
Department of Experimental Medicine (DIMES)
University of Genova, Via Antonio Pastore 3
16132 Genova, Italy
Tel: +3901035338217
Fax: +3901035338215
E-mail: [email protected]

Received date: June 30, 2013; Accepted date: July 22, 2013; Published date: July 25, 2013

Citation: Bragazzi NL, Nicolini C (2013) A Leader Genes Approach-based Tool for Molecular Genomics: From Gene-ranking to Gene-network Systems Biology and Biotargets Predictions. J Comput Sci Syst Biol 6:165-176. doi:10.4172/jcsb.1000113

Copyright: © 2013 Bragazzi NL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Nanogenomics, being the interplay of nanobiotechnologies and bioinformatics, is emerging as an intriguing approach in the field of nowadays biomedicine. Microarrays can produce a wealth of data and details, but they need an algorithm for data reduction to be clearly understood and exploited. The Leader Genes approach, integrating the different available databases and genomics tools, enables the user to search for genes linked to a disease or a cellular process, and to visualize the class of the most important genes, that is to say those having the highest number of interconnections. In this manuscript, we will review the algorithm which has been validated with both experimental and clinical studies. We will describe the different steps that lead to its final version, and we will discuss future perspectives and developments.

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