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.

The cancer bioMarkers database is curated and maintained by several clinical and scientific experts in the field of precision oncology supported by the European Union’s Horizon 2020 funding. This database is currently being integrated with knowledge databases of other institutions in a collaborative effort of the Global Alliance for Genomics and Health.

A tool aimed at the rational design of cancer gene panels. It estimates the cost-effectiveness of the designed panel on a cohort of tumors and provides reports on the importance of individual mutations for tumorigenesis or therapy.

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.

Method to identify genomic regions, both coding and non-coding, bearing mutations with significant shift towards high functional impact across a cohort of tumos (FMbias), which are candidates to function as cancer drivers, through a local test.

OncodriveCLUST is a method aimed to identify genes whose mutations are biased towards a large spatial clustering. This method is designed to exploit the feature that mutations in cancer genes, especially oncogenes, often cluster in particular positions of the protein. We consider this as a sign that mutations in these regions change the function of these proteins in a manner that provides an adaptive advantage to cancer cells and consequently are positively selected during clonal evolution of tumours, and this property can thus be used to nominate novel candidate driver genes./nThe method does not assume that the baseline mutation probability is homogeneous across all gene positions but it creates a background model using silent mutations. Coding silent mutations are supposed to be under no positive selection and may reflect the baseline clustering of somatic mutations. Given recent evidences of non-random mutation processes along the genome, the assumption of homogenous mutation probabilities is likely an oversimplication introducing bias in the detection of meaningful events.

OncodriveFM detects candidate cancer driver genes and pathways from catalogs of somatic mutations in a cohort of tumors by computing the bias towards the accumulation of functional mutations (FM bias).This novel approach avoids some known limitations of recurrence-based approaches, such as the dif?culty to estimate background mutation rate, and the fact that they usually fail to identify lowly recurrently mutated driver genes.

Machine-learning based approach to classify cancer driver genes into to Activating or Loss of Function roles for cancer gene development.

Javascript library to create interactive heatmaps within webpages.