Publications

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Book Chapter
Dopazo J. Clustering - Class discovery in the post-genomic era. In: Fundamentals of data mining in genomics and proteomics. Fundamentals of data mining in genomics and proteomics. New York, USA: Springer-Verlag, W. Dubitzky, M. Granzow and D.P. Berrar; 2007.
Azuaje F, Dopazo J. Data analysis and visualisation in genomics and proteomics. In: Wiley, F. Azuaje and J. Dopazo; 2005.
Azuaje F, Dopazo J, Wang H. Data and Predictive Model Integration: an Overview of Key Concepts, Problems and Solutions. In: Data analysis and visualisation in genomics and proteomics. Data analysis and visualisation in genomics and proteomics. Wiley, F. Azuaje and J. Dopazo; 2005.
Robledo M, González-Neira A, Dopazo J. f single nucleotide polymorphism arrays: Design, tools and applications. In: Microarray Technology Through Applications. Microarray Technology Through Applications. New York, USA: Taylor & Francis, F. Falciani; 2007.
Dopazo J, Al-Shahrour F. Functional annotation of microarray experiments. In: Microarray Technology Through Applications. Microarray Technology Through Applications. New York, USA: Taylor & Francis, F. Falciani; 2007.
Wang H, Azuaje F, Bodenreider O, Dopazo J. Gene expression Correlation and Gene Ontology-Based Similarity: An Assessment of Quantitative Relationship. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.; 2004:25-31.
Azuaje F, Dopazo J. Integrative Data Analysis and Visualization: Introduction to Critical Problems, Goals and Challenges. In: Data analysis and visualisation in genomics and proteomics. Data analysis and visualisation in genomics and proteomics. Wiley, F. Azuaje and J. Dopazo; 2005:3-9.
Dopazo J. Microarray Data Processing And Analysis. In: Microarray data analysis II. Microarray data analysis II. Kluwer Academic; 2002:43-63.
Montaner D, Al-Shahrour F, Dopazo J. New Trends in the Analysis of Functional Genomic Data. In: Progress in Industrial Mathematics at ECMI 2006.Vol 12. Progress in Industrial Mathematics at ECMI 2006. Berlin: Springer; 2007:576-580. doi:10.1007/978-3-540-71992-2_94.
Al-Shahrour F, Dopazo J. Ontologies and functional genomics. In: Data analysis and visualisation in genomics and proteomics. Data analysis and visualisation in genomics and proteomics. Wiley, F. Azuaje and J. Dopazo; 2005:99-102.
Minguez P, Dopazo J. Protein Interactions for Functional Genomics. In: Li X-L, Ng S-K, eds. Biological Data Mining in Protein Interaction Networks. Biological Data Mining in Protein Interaction Networks. Hershey, USA: Idea Group Inc (IGI); 2009:223-238. Available at: http://books.google.es/books?id=pNyCy5GsqtkC.
Antón J, Peña A, Valens M, et al. Salinibacter ruber: genomics and biogeography. In: Adaptation to life in high salt concentrations in Archaea, Bacteria and Eukarya.Vol 9. Adaptation to life in high salt concentrations in Archaea, Bacteria and Eukarya. Dordrecht, Netherlands: Nina Gunde-Cimerman, Ana Plemenitas, and Aharon Oren. Kluwer Academic Publishers; 2005:257-266.
Mateos A, Herrero J, Tamames J, Dopazo J. Supervised Neural Networks For Clustering Conditions In DNA Array Data After Reducing Noise By Clustering Gene Expression Profiles. In: Microarray data analysis II. Microarray data analysis II. Kluwer Academic; 2002:91-103.
Díaz-Uriarte R, Al-Shahrour F, Dopazo J. Use of GO Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data. In: Microarray data analysis III. Microarray data analysis III. Kluwer Academic, K. F. Johnson and S. M. Lin; 2003:233-247.
Al-Shahrour F, Herrero J, Mateos A, Santoyo J, Díaz-Uriarte R, Dopazo J. Using Gene Ontology on genome-scale studies to find significant associations of biologically relevant terms to group of genes. In: Neural Networks for Signal Processing XIII. Neural Networks for Signal Processing XIII. New York, USA: IEEE Press; 2003:43-52.
Mateos A, Herrero J, Dopazo J. Using perceptrons for supervised classification of DNA microarray samples: obtaining the optimal level of information and finding differentially expressed genes. In: ICANN 2002, LNCS 2415. ICANN 2002, LNCS 2415. J.R. Dorronsoro; 2002:577-582.
Conference Paper
Conde L, Mateos A, Herrero J, Dopazo J. Unsupervised reduction of the dimensionality followed by supervised learning with a perceptron improves the classification of conditions in DNA microarray gene expression data. In: Neural Networks for Signal Processing XII. 2002 IEEE Signal Processing Society WorkshopProceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing. Neural Networks for Signal Processing XII. 2002 IEEE Signal Processing Society WorkshopProceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing. Martigny, Switzerland: IEEE; 2002. doi:10.1109/NNSP.2002.1030019.
Journal Article
E. Lewintre J, C. Martin R, Montaner D, et al. Analysis of chronic lymphotic leukemia transcriptomic profile: differences between molecular subgroups. Leuk Lymphoma. 2009;50:68-79. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19127482.
Marti-Renom MA, Rossi A, Al-Shahrour F, et al. The AnnoLite and AnnoLyze programs for comparative annotation of protein structures. BMC Bioinformatics. 2007;8 Suppl 4:S4. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17570147.
Dopazo J, Mendoza A, Herrero J, et al. Annotated draft genomic sequence from a Streptococcus pneumoniae type 19F clinical isolate. Microb Drug Resist. 2001;7:99-125. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11442348.
Herrero J, Diaz-Uriarte R, Dopazo J. An approach to inferring transcriptional regulation among genes from large-scale expression data. Comp Funct Genomics. 2003;4:148-54. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18629097.
Ruiz-Llorente S, Montero-Conde C, Milne RL, et al. Association study of 69 genes in the ret pathway identifies low-penetrance loci in sporadic medullary thyroid carcinoma. Cancer Res. 2007;67:9561-7. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17909067.
Al-Shahrour F, Minguez P, Vaquerizas JM, Conde L, Dopazo J. BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Res. 2005;33:W460-4. Available at: http://nar.oxfordjournals.org/content/33/suppl_2/W460.long.
Al-Shahrour F, Minguez P, Tarraga J, et al. BABELOMICS: a systems biology perspective in the functional annotation of genome-scale experiments. Nucleic Acids Res. 2006;34:W472-6. Available at: http://nar.oxfordjournals.org/content/34/suppl_2/W472.long.
Al-Shahrour F, Carbonell J, Minguez P, et al. Babelomics: advanced functional profiling of transcriptomics, proteomics and genomics experiments. Nucleic Acids Res. 2008;36:W341-6. Available at: http://nar.oxfordjournals.org/content/36/suppl_2/W341.long.