Renz, J., Dauda, K. A., Aga, O. N. L., Diaz-Uriarte, R., Löhr, I. H., Blomberg, B., & Johnston, I. G. (2025). Evolutionary accumulation modeling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance. MBio, 16(6). https://doi.org/10.1128/mbio.00488-25

Renz, J., Dauda, K. A., Aga, O. N. L., Diaz-Uriarte, R., Löhr, I. H., Blomberg, B., & Johnston, I. G. (2024). Evolutionary accumulation modelling in AMR: machine learning to infer and predict evolutionary dynamics of multi-drug resistance (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2411.00219

Aga, O. N. L., Brun, M., Dauda, K. A., Diaz-Uriarte, R., Giannakis, K., & Johnston, I. G. (2024). HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures. https://doi.org/10.1101/2024.03.07.583841

Johnston, I. G., & Diaz-Uriarte, R. (2024). A hypercubic Mk model framework for capturing reversibility in disease, cancer, and evolutionary accumulation modelling. https://doi.org/10.1101/2024.06.27.600959

Diaz-Uriarte, R., & Johnston, I. G. (2023). A picture guide to cancer progression and monotonic accumulation models: evolutionary assumptions, plausible interpretations, and alternative uses. ArXiv. https://doi.org/10.48550/ARXIV.2312.06824

Diaz-Colunga, J., Skwara, A., Gowda, K., Diaz-Uriarte, R., Tikhonov, M., Bajic, D., & Sanchez, A. (2023). Global epistasis on fitness landscapes. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1877). https://doi.org/10.1098/rstb.2022.0053

Fontaneda, D., & Diaz-Uriarte, R. (2023). Adaptive therapy in cancer: the role of restrictions in the accumulation of mutations. https://doi.org/10.1101/2023.05.18.541330

Diaz-Uriarte, R., & Herrera-Nieto, P. (2022). EvAM-Tools: tools for evolutionary accumulation and cancer progression models. https://doi.org/10.1101/2022.07.05.498481

Diaz-Uriarte, R., Gómez de Lope, E., Giugno, R., Fröhlich, H., Nazarov, P. V., Nepomuceno-Chamorro, I. A., Rauschenberger, A., & Glaab, E. (2022). Ten quick tips for biomarker discovery and validation analyses using machine learning. PLOS Computational Biology, 18(8), e1010357. https://doi.org/10.1371/journal.pcbi.1010357

Manrubia, S., Cuesta, J. A., Aguirre, J., Ahnert, S. E., Altenberg, L., Cano, A. V., Catalán, P., Diaz-Uriarte, R., Elena, S. F., García-Martín, J. A., Hogeweg, P., Khatri, B. S., Krug, J., Louis, A. A., Martin, N. S., Payne, J. L., Tarnowski, M. J., & Weiß, M. (2022). The long and winding road to understanding organismal construction. Physics of Life Reviews, 42, 19–24. https://doi.org/10.1016/j.plrev.2022.05.007

Diaz-Uriarte, R. (2021). Simulating Evolution in Asexual Populations with Epistasis. Epistasis, 121–154. https://doi.org/10.1007/978-1-0716-0947-7_9

Manrubia, S., Cuesta, J. A., Aguirre, J., Ahnert, S. E., Altenberg, L., Cano, A. V., Catalán, P., Diaz-Uriarte, R., Elena, S. F., García-Martín, J. A., Hogeweg, P., Khatri, B. S., Krug, J., Louis, A. A., Martin, N. S., Payne, J. L., Tarnowski, M. J., & Weiß, M. (2020). From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics. ArXiv. https://doi.org/10.48550/ARXIV.2002.00363

Diaz-Colunga, J., & Diaz-Uriarte, R. (2020). Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next? https://doi.org/10.1101/2020.12.16.423099

Hosseini, S.-R., Diaz-Uriarte, R., Markowetz, F., & Beerenwinkel, N. (2019). Estimating the predictability of cancer evolution. Bioinformatics, 35(14), i389–i397. https://doi.org/10.1093/bioinformatics/btz332

Diaz-Uriarte, R., & Vasallo, C. (2018). Every which way? On predicting tumor evolution using cancer progression models. https://doi.org/10.1101/371039

Diaz-Uriarte, R. (2017). Cancer progression models and fitness landscapes: a many-to-many relationship. https://doi.org/10.1101/141465

Diaz-Uriarte, R. (2017). OncoSimulR: genetic simulation with arbitrary epistasis and mutator genes in asexual populations. Bioinformatics, 33(12), 1898–1899. https://doi.org/10.1093/bioinformatics/btx077

Diaz-Uriarte, R. (2016). OncoSimulR: genetic simulation of cancer progression with arbitrary epistasis and mutator genes. https://doi.org/10.1101/069500

Moneo, V., Serelde, B. G., Blanco-Aparicio, C., Diaz-Uriarte, R., Avilés, P., Santamaría, G., Tercero, J. C., Cuevas, C., & Carnero, A. (2014). Levels of active tyrosine kinase receptor determine the tumor response to Zalypsis. BMC Cancer, 14(1). https://doi.org/10.1186/1471-2407-14-281

Fernandez‐Navarro, P., González‐Neira, A., Pita, G., Díaz‐Uriarte, R., Tais Moreno, L., Ederra, M., Pedraz‐Pingarrón, C., Sánchez‐Contador, C., Vázquez‐Carrete, J. A., Moreo, P., Vidal, C., Salas‐Trejo, D., Stone, J., Southey, M. C., Hopper, J. L., Pérez‐Gómez, B., Benitez, J., & Pollan, M. (2014). Genome wide association study identifies a novel putative mammographic density locus at 1q12‐q21. International Journal of Cancer, 136(10), 2427–2436. Portico. https://doi.org/10.1002/ijc.29299

Diaz-Uriarte, R. (2014). Identifying Restrictions in the Order of Accumulation of Mutations during Tumor Progression: Effects of Passengers, Evolutionary Models, and Sampling. https://doi.org/10.1101/005587

Diaz-Uriarte, R. (2014). ADaCGH2: parallelized analysis of (big) CNA data. Bioinformatics, 30(12), 1759–1761. https://doi.org/10.1093/bioinformatics/btu099

Rueda, O. M., Diaz-Uriarte, R., & Caldas, C. (2013). Finding Common Regions of Alteration in Copy Number Data. Array Comparative Genomic Hybridization, 339–353. https://doi.org/10.1007/978-1-62703-281-0_21

Rueda, O. M., Rueda, C., & Diaz-Uriarte, R. (2013). A Bayesian HMM with random effects and an unknown number of states for DNA copy number analysis. Journal of Statistical Computation and Simulation, 83(1), 82–96. https://doi.org/10.1080/00949655.2011.609818

Landa, I., Boullosa, C., Inglada-Pérez, L., Sastre-Perona, A., Pastor, S., Velázquez, A., Mancikova, V., Ruiz-Llorente, S., Schiavi, F., Marcos, R., Malats, N., Opocher, G., Diaz-Uriarte, R., Santisteban, P., Valencia, A., & Robledo, M. (2013). An Epistatic Interaction between the PAX8 and STK17B Genes in Papillary Thyroid Cancer Susceptibility. PLoS ONE, 8(9), e74765. https://doi.org/10.1371/journal.pone.0074765

Landa, I., Boullosa, C., Inglada-Pérez, L., Sastre-Perona, A., Pastor, S., Velázquez, A., Mancikova, V., Ruiz-Llorente, S., Schiavi, F., Marcos, R., Malats, N., Opocher, G., Diaz-Uriarte, R., Santisteban, P., Valencia, A., & Robledo, M. (2013). Correction: An Epistatic Interaction between the PAX8 and STK17B Genes in Papillary Thyroid Cancer Susceptibility. PLoS ONE, 8(10). https://doi.org/10.1371/annotation/cd94c3eb-70f2-4dfa-85be-b3fc41e495c3

Barderas, R., Babel, I., Díaz-Uriarte, R., Moreno, V., Suárez, A., Bonilla, F., Villar-Vázquez, R., Capellá, G., & Casal, J. I. (2012). An optimized predictor panel for colorectal cancer diagnosis based on the combination of tumor-associated antigens obtained from protein and phage microarrays. Journal of Proteomics, 75(15), 4647–4655. https://doi.org/10.1016/j.jprot.2012.03.004

Babel, I., Barderas, R., Diaz-Uriarte, R., Moreno, V., Suarez, A., Fernandez-Aceñero, M. J., Salazar, R., Capellá, G., & Casal, J. I. (2011). Identification of MST1/STK4 and SULF1 Proteins as Autoantibody Targets for the Diagnosis of Colorectal Cancer by Using Phage Microarrays. Molecular & Cellular Proteomics, 10(3), M110.001784. https://doi.org/10.1074/mcp.m110.001784

Subirana, I., Diaz-Uriarte, R., Lucas, G., & Gonzalez, J. R. (2011). CNVassoc: Association analysis of CNV data using R. BMC Medical Genomics, 4(1). https://doi.org/10.1186/1755-8794-4-47

Montes-Moreno, S., Martinez, N., Sanchez-Espiridión, B., Díaz Uriarte, R., Rodriguez, M. E., Saez, A., Montalbán, C., Gomez, G., Pisano, D. G., García, J. F., Conde, E., Gonzalez-Barca, E., Lopez, A., Mollejo, M., Grande, C., Martinez, M. A., Dunphy, C., Hsi, E. D., Rocque, G. B., … Piris, M. A. (2011). miRNA expression in diffuse large B-cell lymphoma treated with chemoimmunotherapy. Blood, 118(4), 1034–1040. https://doi.org/10.1182/blood-2010-11-321554

Rico, D., Earl, J., Diaz-Uriarte, R., Rueda, O. M., Marenne, G., & Pita, G. (2010). Genomic copy number alterations in cancer: high-resolution analysis of the NCI-60 panel. New Biotechnology, 27, S81. https://doi.org/10.1016/j.nbt.2010.01.228

Moneo, V., Diaz-Uriarte, R., Serelde, B., Blanco-Aparicio, C., Aviles, P., Gema, S., Tercero, J. C., Cuevas, C., & Carnero, A. (2010). Abstract 1685: Identification of PDGFR-a as a predictive biomarker for response to Zalypsis®. Cancer Research, 70(8_Supplement), 1685–1685. https://doi.org/10.1158/1538-7445.am10-1685

Carro, A., Rico, D., Rueda, O. M., D�az-Uriarte, R., & Pisano, D. G. (2010). waviCGH: a web application for the analysis and visualization of genomic copy number alterations. Nucleic Acids Research, 38(suppl_2), W182–W187. https://doi.org/10.1093/nar/gkq441

Fuxjager, M. J., Foufopoulos, J., Diaz‐Uriarte, R., & Marler, C. A. (2010). Functionally opposing effects of testosterone on two different types of parasite: implications for the immunocompetence handicap hypothesis. Functional Ecology, 25(1), 132–138. Portico. https://doi.org/10.1111/j.1365-2435.2010.01784.x

Sequeira-Mendes, J., Díaz-Uriarte, R., Apedaile, A., Huntley, D., Brockdorff, N., & Gómez, M. (2009). Transcription Initiation Activity Sets Replication Origin Efficiency in Mammalian Cells. PLoS Genetics, 5(4), e1000446. https://doi.org/10.1371/journal.pgen.1000446

Morrissey, E. R., & Diaz-Uriarte, R. (2009). Pomelo II: finding differentially expressed genes. Nucleic Acids Research, 37(Web Server), W581–W586. https://doi.org/10.1093/nar/gkp366

Rueda, O. M., & Diaz-Uriarte, R. (2009). RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions. Bioinformatics, 25(15), 1959–1960. https://doi.org/10.1093/bioinformatics/btp307

Babel, I., Barderas, R., Díaz-Uriarte, R., Martínez-Torrecuadrada, J. L., Sánchez-Carbayo, M., & Casal, J. I. (2009). Identification of Tumor-associated Autoantigens for the Diagnosis of Colorectal Cancer in Serum Using High Density Protein Microarrays. Molecular & Cellular Proteomics, 8(10), 2382–2395. https://doi.org/10.1074/mcp.m800596-mcp200

Diaz-Uriarte, R. (2008). SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data. BMC Bioinformatics, 9(1). https://doi.org/10.1186/1471-2105-9-30

Alibes, A., Canada, A., & Diaz-Uriarte, R. (2008). PaLS: filtering common literature, biological terms and pathway information. Nucleic Acids Research, 36(Web Server), W364–W367. https://doi.org/10.1093/nar/gkn251

Urdinguio, R. G., Lopez-Serra, L., Lopez-Nieva, P., Alaminos, M., Diaz-Uriarte, R., Fernandez, A. F., & Esteller, M. (2008). Mecp2-Null Mice Provide New Neuronal Targets for Rett Syndrome. PLoS ONE, 3(11), e3669. https://doi.org/10.1371/journal.pone.0003669

Diaz-Uriarte, R. (2007). GeneSrF and varSelRF: a web-based tool and R package for gene selection and classification using random forest. BMC Bioinformatics, 8(1). https://doi.org/10.1186/1471-2105-8-328

Rueda, O. M., & Díaz-Uriarte, R. (2007). Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Computational Biology, 3(6), e122. https://doi.org/10.1371/journal.pcbi.0030122

Rueda, O. M., & Diaz-Uriarte, R. (2007). A response to Yu et al. “A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array”, BMC Bioinformatics 2007, 8: 145. BMC Bioinformatics, 8(1). https://doi.org/10.1186/1471-2105-8-394

Moneo, V., Serelde, B. G., Leal, J. F. M., Blanco-Aparicio, C., Diaz-Uriarte, R., Aracil, M., Tercero, J. C., Jimeno, J., & Carnero, A. (2007). Levels of p27kip1 determine Aplidin sensitivity. Molecular Cancer Therapeutics, 6(4), 1310–1316. https://doi.org/10.1158/1535-7163.mct-06-0729

Diaz-Uriarte, R., Alibes, A., Morrissey, E. R., Canada, A., Rueda, O. M., & Neves, M. L. (2007). Asterias: integrated analysis of expression and aCGH data using an open-source, web-based, parallelized software suite. Nucleic Acids Research, 35(Web Server), W75–W80. https://doi.org/10.1093/nar/gkm229

Rodríguez, A., Villuendas, R., Yáñez, L., Gómez, M. E., Díaz, R., Pollán, M., Hernández, N., de la Cueva, P., Marín, M. C., Swat, A., Ruiz, E., Cuadrado, M. A., Conde, E., Lombardía, L., Cifuentes, F., Gonzalez, M., García-Marco, J. A., & Piris, M. A. (2007). Molecular heterogeneity in chronic lymphocytic leukemia is dependent on BCR signaling: clinical correlation. Leukemia, 21(9), 1984–1991. https://doi.org/10.1038/sj.leu.2404831

Díaz-Uriarte, R., & Rueda, O. M. (2007). ADaCGH: A Parallelized Web-Based Application and R Package for the Analysis of aCGH Data. PLoS ONE, 2(8), e737. https://doi.org/10.1371/journal.pone.0000737

Collado, M., Garcia, V., Garcia, J. M., Alonso, I., Lombardia, L., Diaz-Uriarte, R., López Fernández, L. A., Zaballos, A., Bonilla, F., & Serrano, M. (2007). Genomic Profiling of Circulating Plasma RNA for the Analysis of Cancer. Clinical Chemistry, 53(10), 1860–1863. https://doi.org/10.1373/clinchem.2007.089201

Díaz‐Uriarte, R. (2005). Supervised Methods with Genomic Data: a Review and Cautionary View. Data Analysis and Visualization in Genomics and Proteomics, 193–214. Portico. https://doi.org/10.1002/0470094419.ch12

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(13), 2988–2993. https://doi.org/10.1093/bioinformatics/bti457

Vaquerizas, J. M., Conde, L., Yankilevich, P., Cabezon, A., Minguez, P., Diaz-Uriarte, R., Al-Shahrour, F., Herrero, J., & Dopazo, J. (2005). GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data. Nucleic Acids Research, 33(Web Server), W616–W620. https://doi.org/10.1093/nar/gki500

Diaz-Uriarte, R. (2004). Molecular Signatures from Gene Expression Data (Version 3). arXiv. https://doi.org/10.48550/ARXIV.Q-BIO/0401043

Meléndez, B., Díaz‐Uriarte, R., Cuadros, M., Martínez‐Ramírez, Á., Fernández‐Piqueras, J., Dopazo, A., Cigudosa, J., Rivas, C., Dopazo, J., Martínez‐Delgado, B., & Benítez, J. (2004). Gene expression analysis of chromosomal regions with gain or loss of genetic material detected by comparative genomic hybridization. Genes, Chromosomes and Cancer, 41(4), 353–365. Portico. https://doi.org/10.1002/gcc.20105

Vaquerizas, J. M., Dopazo, J., & Díaz-Uriarte, R. (2004). DNMAD: web-based diagnosis and normalization for microarray data. Bioinformatics, 20(18), 3656–3658. https://doi.org/10.1093/bioinformatics/bth401

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, 20(4), 578–580. https://doi.org/10.1093/bioinformatics/btg455

Herrero, J., Vaquerizas, J. M., Al-Shahrour, F., Conde, L., Mateos, A., Diaz-Uriarte, J. S. R., & Dopazo, J. (2004). New challenges in gene expression data analysis and the extended GEPAS. Nucleic Acids Research, 32(Web Server), W485–W491. https://doi.org/10.1093/nar/gkh421

Herrero, J., Díaz‐Uriarte, R., & Dopazo, J. (2003). An approach to inferring transcriptional regulation among genes from large‐scale expression data. Comparative and Functional Genomics, 4(1), 148–154. Portico. https://doi.org/10.1002/cfg.237

Herrero, J., Díaz-Uriarte, R., & Dopazo, J. (2003). Gene expression data preprocessing. Bioinformatics, 19(5), 655–656. https://doi.org/10.1093/bioinformatics/btg040

Díaz-Uriarte, R. (2001). Territorial intrusion risk and antipredator behaviour: a mathematical model. Proceedings of the Royal Society of London. Series B: Biological Sciences, 268(1472), 1165–1173. https://doi.org/10.1098/rspb.2001.1637

Garland, T., Martin, K. L. M., & Díaz-Uriarte, R. (1997). RECONSTRUCTING ANCESTRAL TRAIT VALUES USING SQUARED-CHANGE PARSIMONY: PLASMA OSMOLARITY AT THE ORIGIN OF AMNIOTES. Amniote Origins, 425–501. https://doi.org/10.1016/b978-012676460-4/50014-7

Bauwens, D., & Diaz-Uriarte, R. (1997). Covariation of Life-History Traits in Lacertid Lizards: A Comparative Study. The American Naturalist, 149(1), 91–111. https://doi.org/10.1086/285980

Diaz, J. A., Diaz-Uriarte, R., & Rodriguez, A. (1996). Influence of Behavioral Thermoregulation on the Use of Vertical Surfaces by Iberian Wall Lizards Podarcis hispanica. Journal of Herpetology, 30(4), 548. https://doi.org/10.2307/1565703

Díaz-Uriarte, R., & Garland, T. (1996). Testing Hypotheses of Correlated Evolution Using Phylogenetically Independent Contrasts: Sensitivity to Deviations from Brownian Motion. Systematic Biology, 45(1), 27–47. https://doi.org/10.1093/sysbio/45.1.27

Al-Shahrour, F., Herrero, J., Mateos, A., Santoyo, J., Diaz-Uriarte, R., & Dopazo, J. (n.d.). Using gene ontology on genome-scale studies to find significant associations of biologically relevant terms to groups of genes. 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), 43–52. https://doi.org/10.1109/nnsp.2003.1318003

Díaz-Uriarte, R., Al-Shahrour, F., & Dopazo, J. (n.d.). The Use of Go Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data. Methods of Microarray Data Analysis III, 233–247. https://doi.org/10.1007/0-306-48354-8_16

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