Abstract
In the last years, systems and computational biology focused their efforts in uncovering the causal relationships among the observable perturbations of gene regulatory networks and human diseases. This problem becomes even more challenging when network models and algorithms have to take into account slightly significant effects, caused by often peripheral or unknown genes that cooperatively cause the observed diseased phenotype. Many solutions, from community and pathway analysis to information flow simulation, have been proposed, with the aim of reproducing biological regulatory networks and cascades, directly from empirical data as gene expression microarray data. In this contribute, we propose a methodology to evaluate the most important shortest paths between differentially expressed genes in biological interaction networks, with absolutely no need of user-defined parameters or heuristic rules, enabling a free-of-bias discovery and overcoming common issues affecting the most recent network-based algorithms.
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Pepe, D., Palluzzi, F., Grassi, M. (2015). Sem Best Shortest Paths for the Characterization of Differentially Expressed Genes. In: DI Serio, C., Liò, P., Nonis, A., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2014. Lecture Notes in Computer Science(), vol 8623. Springer, Cham. https://doi.org/10.1007/978-3-319-24462-4_12
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DOI: https://doi.org/10.1007/978-3-319-24462-4_12
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