Publications

How to cite Babelomics

Medina et al. 2010. Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Research (Web Server issue).

2010

  • Medina I, Carbonell J, Pulido L, Madeira S, Goetz S, Conesa A, Tárraga J, Pascual-Montano A, Nogales-Cadenas R, Santoyo J, García F, Marbà M, Montaner D and Dopazo J (2010). Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Research (Web Server issue)
  • Minguez P, Dopazo J. (2010) Functional genomics and networks: new approaches in the extraction of complex gene modules. Expert Rev Proteomics. 7(1):55-63.
  • Montaner D, Dopazo J. (2010) Multidimensional gene set analysis of genomic data. PLoS ONE. 5(4):e10348

2009

  • Dopazo J. (2009) Formulating and testing hypotheses in functional genomics. Artif Intell Med. 45:97-107.
  • Nueda M J, Sebastián P, Tarazona S, et al. (2009) Functional assessment of time course microarray data. BMC Bioinformatics. 10 Suppl 6:S9.
  • Medina I, Montaner D, Bonifaci N, et al. (2009) Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies. Nucl. Acids Res. 37:W340-344
  • Minguez P, Gotz S, Montaner D, Al-Shahrour F, Dopazo J. (2009) SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks. Nucl. Acids Res. 37:W109-114

2008

  • Al-Shahrour F, Carbonell J, Minguez P, et al. (2008) Babelomics: advanced functional profiling of transcriptomics, proteomics and genomics experiments. Nucleic Acids Res. 36:W341-6
  • Tarraga J, Medina I, Carbonell J, et al. (2008) GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Res. 36:W308-14
  • Valls, J., Grau, M., Sole, X., Hernandez, P., Montaner, D., Dopazo, J., Peinado, M.A., Capella, G., Pujana, M.A., Moreno, V. (2008) CLEAR-test: Combining inference for differential expression and variability in microarray data analysis. J Biomed Inform. 41:33-45.
  • Gotz S, Garcia-Gomez JM, Terol J, et al. (2008) High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 36:3420-35.

2007

  • Minguez P, Al-Shahrour F, Montaner D, Dopazo J. (2007) Functional profiling of microarray experiments using text-mining derived bioentities. Bioinformatics. 23:3098-9
  • Al-Shahrour, F., Minguez, P., Tárraga, J., Medina, I., Alloza, E., Montaner, D., and Dopazo, J. (2007) FatiGO+: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucl Acids Res. 35:W91-6
  • Conde, L., Montaner D., Burguet-Castell, J., Tárraga, J., Medina, I., Al-Shahrour, F. and Dopazo, J. (2007) ISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling. Nucl Acids Res. 35:W81-5
  • Medina I, Montaner D, Tarraga J, Dopazo J. (2007) Prophet, a web-based tool for class prediction using microarray data. Bioinformatics. 23(3):390-1
  • Conde, L., Montaner, D., Burguet-Castell, J., Tarraga, J., Al-Shahrour, F. and Dopazo, J. (2007) Functional profiling and gene expression analysis of chromosomal copy number alterations. Bioinformation 1(10): 432-435
  • Al-Shahrour F, Arbiza L, Dopazo H, Huerta J, Minguez P, Montaner D, and Dopazo J (2007) From genes to functional classes in the study of biological systems. BMC Bioinformatics 8:114

2006

  • Dopazo, J. (2006) Functional Interpretation of Microarray Experiments. OMICS: A Journal of Integrative Biology. 10:398-410
  • Minguez, P., Al-Shahrour, F., Dopazo, J. (2006) A function-centric approach to the biological interpretation of microarray time-series. Genome Informatics Series Vol.17 No.2 (pg 57-66) 2006 ISSN:0919-9454
  • Al-Shahrour F., Minguez P., Tárraga J., Montaner D., Alloza E., Vaquerizas J.M., Conde L., Blaschke C., Vera J. and Dopazo J. BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments. Nucl Acids Res., 2006, 34: W472-W476
  • Montaner D., Tárraga J., Huerta-Cepas J., Burguet J., Vaquerizas J.M., Conde L., Minguez P., Vera J., Mukherjee S., Valls J., Pujana M., Alloza E., Herrero J., Al-Shahrour F., Dopazo J. Next station in microarray data analysis: Babelomics. Nucl Acids Res., 2006, 34: W486-W491

2005

  • Vaquerizas, J.M.,Conde, L., Yankilevich, P., Cabezon, A., Minguez, P., Diaz-Uriarte, R., Al-Shahrour, F., Herrero, J & Dopazo, J. 2005 Babelomics an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Research. 33:W616-W620
  • Al-Shahrour, F., Minguez, P., Vaquerizas, J.M., Conde, L. & Dopazo, J. 2005 Babelomics: a suite of web-tools for functional annotation and analysis of group of genes in high-throughput experiments.

Nucleic Acids Research 33:W460-W464.

  • Al-Shahrour, F., Diaz-Uriarte, R. & Dopazo, J. (2005) Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics.21: 2988-2993.

2004

  • Al-Shahrour, F., Díaz-Uriarte, R. & Dopazo, J. (2004) FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 2004 20: 578-580;
  • Vaquerizas, J.M., Dopazo, J. & Díaz-Uriarte, R. (2004) DNMAD: web-based Diagnosis and Normalization for MicroArray Data. Bioinformatics 20(18), 3656-3658

2003

  • Herrero, J., Díaz-Uriarte, R. & Dopazo, J (2003) Gene Expression Data Preprocessing. Bioinformatics 19(5), 655-656

2002

  • Herrero, J. & Dopazo, J. (2002) Combining Hierarchical Clustering and Self-Organizing Maps for Exploratory Analysis of Gene Expression Patterns. Journal of Proteome Research. 1(5), 467-470

2001

  • Herrero, J., Valencia, A. y Dopazo, J. (2001) A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics, 17(2), 126-136

1997

  • Dopazo J. & Carazo J. M.(1997) Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree. J. Mol. Evol. 44, 226-233.
publications.txt · Last modified: 2010/09/26 15:17 by dmontaner
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