Part 1 - Networks of prior knowledge as frames to understand complex biological data | Vidéo | Carmin.tv

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Part 1 - Networks of prior knowledge as frames to understand complex biological data

De Julio Saez-Rodriguez

Apparaît dans la collection : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaire

Modern technologies allow us to profile in high detail biomedical samples at fast decreasing costs. New technologies are opening new data modalities, in particular to measure at the single cell level. Prior knowledge, and biological networks in particular, are useful to integrate this data and distill mechanistic insight. This can help to interpret the result of machine learning or statistical analysis, as well as generate input features for these methods. In addition, they can be converted in dynamic mechanistic models to gain more specific insight. I will give an overview of these approaches showcasing some examples and approaches used in the field.

Informations sur la vidéo

Date de captation 02/03/2020
Date de publication 23/03/2020
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs
Réalisateur(s) Guillaume Hennenfent
Format MP4

Données de citation

DOI 10.24350/CIRM.V.19619503
Citer cette vidéo Saez-Rodriguez, Julio (02/03/2020). Part 1 - Networks of prior knowledge as frames to understand complex biological data. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19619503
URL https://dx.doi.org/10.24350/CIRM.V.19619503

Domaine(s)

Bibliographie

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CECCARELLI, Francesco, TUREI, Denes, GABOR, Attila, et al. Bringing data from curated pathway resources to Cytoscape with OmniPath. Bioinformatics, 2019. - https://doi.org/10.1093/bioinformatics/btz968
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CHUANG, Han‐Yu, LEE, Eunjung, LIU, Yu‐Tsueng, et al. Network‐based classification of breast cancer metastasis. Molecular systems biology, 2007, vol. 3, no 1. - https://doi.org/10.1038/msb4100180
KHOLODENKO, Boris N., KIYATKIN, Anatoly, BRUGGEMAN, Frank J., et al. Untangling the wires: a strategy to trace functional interactions in signaling and gene networks. Proceedings of the National Academy of Sciences, 2002, vol. 99, no 20, p. 12841-12846. - https://doi.org/10.1073/pnas.192442699
TERFVE, Camille DA, WILKES, Edmund H., CASADO, Pedro, et al. Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data. Nature communications, 2015, vol. 6, p. 8033. - https://doi.org/10.1038/ncomms9033
MORRIS, Melody K., SAEZ-RODRIGUEZ, Julio, CLARKE, David C., et al. Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS computational biology, 2011, vol. 7, no 3. - https://dx.doi.org/10.1371%2Fjournal.pcbi.1001099
WITTMANN, Dominik M., KRUMSIEK, Jan, SAEZ-RODRIGUEZ, Julio, et al. Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC systems biology, 2009, vol. 3, no 1, p. 98. - http://dx.doi.org/10.1186/1752-0509-3-98

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