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This article is part of the supplement: Computational Intelligence in Bioinformatics and Biostatistics: new trends from the CIBB conference series

Open Access Research

SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS

Ivan Merelli1, Andrea Calabria2, Paolo Cozzi13, Federica Viti1, Ettore Mosca1 and Luciano Milanesi1*

Author Affiliations

1 Consiglio Nazionale delle Ricerche - Istituto di Tecnologie Biomediche (CNR-ITB), Via F.lli Cervi 93, 20090 Segrate (MI), Italy

2 San Raffaele Telethon Institute for Gene Therapy (HSR-TIGET), Via Olgettina 58, 20132 Milano, Italy

3 Parco Tecnologico Padano, Via Einstein - Loc. Cascina Codazza, 26900 Lodi, Italy

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BMC Bioinformatics 2013, 14(Suppl 1):S9  doi:10.1186/1471-2105-14-S1-S9

Published: 14 January 2013

Additional files

Additional File 1:

SNPranker 2.0 features set. All the available SNP features at SNPranker 2.0 web site, grouped in semantic sections.

Format: PDF Size: 75KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 2:

The OMIM diseases employed for the machine learning approach. The table shows the list of diseases employed for training the scoring algorithm, providing information about the genomics regions, the disease names, the OMIM disease IDs, and the involved genes, summarized as gene symbols and Entrez IDs.

Format: PDF Size: 92KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 3:

Results of the genetic algorithm optimization process. For each disease of the training set, the table summarizes SNP counts, sensitivity, specificity and accuracy achieved with the optimal feature weights found with the genetic algorithm.

Format: PDF Size: 100KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data