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Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 8th International Me Elia Biganzoli,Alfredo Vellido,Roberto Taglia

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Maria Overbeck-Larisch,Wolfgang Dolejskytwork (SNN) model at a higher level to model the dynamic interaction between genes and spiking patterns of activity under certain conditions. The paper demonstrates that it is possible to model and trace over time the effect of a gene on the total spiking behavior of the SNN when the gene controls a
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https://doi.org/10.1007/978-3-658-33779-7 identify the nature of differential expression (e.g., gene is differentially expressed between the first and the second sample but is not differentially expressed between the second and the third) and subsequently we estimate gene expression temporal profiles. The proposed procedure deals successfu
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Norbert Henze,Kai Müller,Judith Schillingystems biology. In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating ke
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,Stochastik – eine Wissenschaft für sich, problem to store, manage and analyze terabytes of sequencing data often generated from extremely different data-sources. Our project is mainly focused on the sequence analysis of human cancer genomes, in order to identify the genetic lesions underlying the development of tumors. However, the automa
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