BUSCADOR RECOLECTA

This dataset provides you with the source codes and results underlying the manuscript: Espinoso A and Andrzejak RG. 2022. Phase irregularity: A conceptually simple and efficient approach to characterize electroencephalographic recordings from epilepsy patients. Phys. Rev. E. 105, 034212. In order to understand the source code, you should first read the manuscript. All the detailed information explaining how to use the source code and how the results are organized can be found in the readme file. The code is done using MATLAB and it uses the same notation for mathematical symbols as in the manuscript. Additional comments can be found throughout the source code. The code files starting by "EA" are done for obtaining the results of the manuscript Espinoso 2022. The code files starting by "ASR" were used for obtaining surrogates and filter the signals. If you use these last mentioned codes, please cite the following paper: Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E, 86, 046206, 2012 To get started with the code for obtaining the EEG results open EA_EEG_Main.m, and for the Rössler results open EA_Rossler_Main.m. We have tested the code, and to the best of our knowledge it has no bugs. In case you have any questions or problems, please contact us. The same results obtained in the paper for the EEG and Rössler are available in MATLAB format. Please, read the important remark in the readme file. If you run the analysis using the codes provided here, you will not get identical results as in the paper. This is the reason why we provide the identical results here.

Analyses somatic mutations in thousands of tumor genomes to identify cancer driver genes.

Gitools is a framework for analysis and visualization of multidimensional genomic data using interactive heat-maps.

This database contains information on the genes identified as drivers in Rubio-Perez and Tamborero et al. (2015). It contains information on driver identification at mutational, CNA and gene fusion level. Additional ancillary information about the role and major clonality of drivers is also included. A table is also provided with the list of datasets used for mutational driver identification.

This file lists the High Confidence Drivers identified as part of the pan-cancer12 initiative, published in the paper Comprehensive identification of mutational cancer driver genes across 12 tumor types" Scientific Reports 3:2650, 2013, doi:10.1038/srep02650

Mutations, genes and pathways involved in tumorigenesis across 4,623 cancer genomes/exomes from 13 cancer sites. IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods 10, 2013, doi:10.1038/nmeth.2642

This page provides the source code underlying the manuscript: Leguia MG, Martínez CGB, Malvestio I, Campo A T, Rocamora R, Levnajic Z, Andrzejak RG. 2019. Inferring directed networks using a rank-based connectivity measure. Phys. Rev. E. 99, 012319 If you use any of these resources, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads

This page provides the source code and results underlying the manuscript: Malvestio I, Kreuz T, Andrzejak RG. 2017. Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains. Physical Review E 96, 022203. If you use any of these resources, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads

This page provides the source code underlying the manuscript: Andrzejak RG, Ruzzene G, Malvestio I. 2017. Generalized synchronization between chimera states. Chaos. 27(5): 053114 If you use any of these resources, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads

This page data analyzed in the manuscript: Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE (2001) Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64, 061907. If you use any of these resources, please make sure that you cite this reference. For more detailed information, please refer to https://www.upf.edu/web/ntsa/downloads