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Journal Article
Ariño J, Casamayor A, Pérez JPerez, et al. Assessing Differential Expression Measurements by Highly Parallel Pyrosequencing and DNA Microarrays: A Comparative Study. Omics : a journal of integrative biology. 2013. doi:10.1089/omi.2011.0065.
Nueda MJ, Ferrer A, Conesa A. ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics (Oxford, England). 2011.
Loucera-Muñecas C, Canal-Rivero M, Ruiz-Veguilla M, et al. Aripiprazole as protector against COVID-19 mortality. Sci Rep. 2024;14(1):12362. doi:10.1038/s41598-024-60297-y.
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.
Casimiro-Soriguer CS, Loucera C, Florido JPerez, López-López D, Dopazo J. Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples. Biol Direct. 2019;14(1):15. doi:10.1186/s13062-019-0246-9.
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.
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.
Trevino V, Tadesse MG, Vannucci M, et al. Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells. PloS one. 2011;6:e16492.
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.
Terol J, Conesa A, Colmenero JM, et al. Analysis of 13000 unique Citrus clusters associated with fruit quality, production and salinity tolerance. BMC Genomics. 2007;8:31. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17254327.
Boxma B, de Graaf RM, van der Staay GW, et al. An anaerobic mitochondrion that produces hydrogen. Nature. 2005;434:74-9. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15744302.
Marti-Renom MA, Madhusudhan MS, Sali A. Alignment of protein sequences by their profiles. Protein Sci. 2004;13:1071-87. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15044736.
Madhusudhan MS, Webb BM, Marti-Renom MA, Eswar N, Sali A. Alignment of multiple protein structures based on sequence and structure features. Protein engineering, design & selection : PEDS. 2009;22:569-74.
Iglesias JManuel, Leis O, Ruiz EPérez, et al. The Activation of the Sox2 RR2 Pluripotency Transcriptional Reporter in Human Breast Cancer Cell Lines is Dynamic and Labels Cells with Higher Tumorigenic Potential. Front Oncol. 2014;4:308. doi:10.3389/fonc.2014.00308.
Salavert F, Hidago MR, Amadoz A, et al. Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models. Nucleic acids research. 2016. doi:10.1093/nar/gkw369.
Melo F, Marti-Renom MA. Accuracy of sequence alignment and fold assessment using reduced amino acid alphabets. Proteins. 2006;63:986-95. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16506243.
Tárraga J, Arnau V, Martinez H, et al. Acceleration of short and long DNA read mapping without loss of accuracy using suffix array. Bioinformatics (Oxford, England). 2014;30:3396-3398. doi:10.1093/bioinformatics/btu553.
Dopazo J, Amadoz A, Bleda M, et al. 267 Spanish exomes reveal population-specific differences in disease-related genetic variation. Molecular biology and evolution. 2016. doi:10.1093/molbev/msw005.
Golubnitschaja O, Topolcan O, Kucera R, Costigliola V. 10th Anniversary of the European Association for Predictive, Preventive and Personalised (3P) Medicine - EPMA World Congress Supplement 2020. EPMA J. 2020:1-133. doi:10.1007/s13167-020-00206-1.
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.
Silbiger V, Luchessi A, Hirata R, et al. Peripheral blood cells transcriptome to study new biomarkers for myocardial infarction follow up. In: ; 2009.
Gonzalez CY, Bleda M, Salavert F, Sánchez R, Dopazo J, Medina I. Multicore and Cloud-based Solutions for Genomic Variant Analysis. In: Proceedings of the 18th International Conference on Parallel Processing Workshops. Proceedings of the 18th International Conference on Parallel Processing Workshops. Berlin, Heidelberg: Springer-Verlag; 2013. doi:10.1007/978-3-642-36949-0_30.
Book Chapter
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.
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.
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.