A pipeline to create predictive functional networks: application to the tumor progression of hepatocellular carcinoma

Background Integrating genome-wide gene expression patient profiles with regulatory knowledge is a challenging task because of the inherent heterogeneity, noise and incompleteness of biological data. From the computational side, several solvers for logic programs are able to perform extremely well i...

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Vydané v:	BMC bioinformatics Ročník 21; číslo 1; s. 18 - 14
Hlavní autori:	Folschette, Maxime, Legagneux, Vincent, Poret, Arnaud, Chebouba, Lokmane, Guziolowski, Carito, Théret, Nathalie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 14.01.2020 Springer Nature B.V BMC
Predmet:	Algorithms Bcl-3 protein Biochemistry, Molecular Biology Bioinformatics Biological activity Biological computing Biomedical and Life Sciences Biomolecules Cancer Carcinoma, Hepatocellular - genetics Combinatorial analysis Complex systems Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Computer Science Data and network integration Databases, Genetic Discrete modeling Disease Progression Gene expression Gene Regulatory Networks Genes Genomes Graph theory Hepatocellular carcinoma Heterogeneity Humans KEGG Life Sciences Linear programming Liver cancer Liver Neoplasms - genetics Logic programming Logic programs Microarrays Modeling and Simulation Molecular Networks Pipelines Prediction models Proteins Research Article Signaling and regulatory knowledge Solvers Stability analysis Transcriptome Workflow Signaling and regulatory knowledge Hepatocellular carcinoma KEGG Discrete modeling Data and network integration
ISSN:	1471-2105, 1471-2105
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