CIRM - Videos & books Library - Part 1 - Networks of prior knowledge as frames to understand complex biological data

Multi angle

Auteurs : Saez-Rodriguez, Julio (Auteur de la conférence)
CIRM (Editeur )

Loading the player...

Résumé : 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.

Mots-Clés : biological network; bio-informatics

Codes MSC :
92-08 - Computational methods
92B05 - General biology and biomathematics
92C42 - Systems biology, networks
92-10 - Mathematical modeling or simulation for problems pertaining to biology

Informations sur la Vidéo

Réalisateur :

Hennenfent, Guillaume

Langue :

Date de Publication :

Date de Captation :

Sous Collection :

Catégorie arXiv :

Domaine(s) :

Format :

Durée :

Audience :

Download :

https://videos.cirm-math.fr/2020-03-02_Saez-Rodriguez _Part1.mp4

Informations sur la Rencontre

Nom de la Rencontre : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaire
Organisateurs de la Rencontre : Baudot, Anais ; Hubert, Florence ; Moss, Brigitte ; Rémy, Elisabeth ; Tichit, Laurent ; Vignes, Matthieu
Dates : 02/03/2020 - 06/03/2020
Année de la rencontre : 2020
URL de la Rencontre : https://conferences.cirm-math.fr/2305.html

Données de citation

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

Voir Aussi

[Multi angle] Part 2 - Dynamic logic models complement machine learning for personalized medicine / Auteur de la conférence Saez-Rodriguez, Julio.
[Multi angle] Matrix factorisation techniques for data integration / Auteur de la conférence Lê Cao, Kim-Anh.
[Post-edited] Network-guided feature selection in high-dimensional genomic data / Auteur de la conférence Azencott, Chloé.
[Multi angle] Cell processes in space and time - exemplified with yeast mating / Auteur de la conférence Klipp, Edda.
[Multi angle] Network approaches for personalized medicine / Auteur de la conférence Martinez-Rodriguez, Maria.
[Multi angle] Learning interpretable networks from multivariate information in biological and clinical data / Auteur de la conférence Isambert, Hervé.
[Multi angle] From data to biological processes: interactions, networks and analyses, to understand cellular functions / Auteur de la conférence Brun, Christine.

Bibliographie

SAEZ-RODRIGUEZ, Julio, RINSCHEN, Markus M., FLOEGE, Jürgen, et al. Big science and big data in nephrology. Kidney international, 2019. - https://doi.org/10.1016/j.kint.2018.11.048

KAWATA, Kentaro, HATANO, Atsushi, YUGI, Katsuyuki, et al. Trans-omic analysis reveals selective responses to induced and basal insulin across signaling, transcriptional, and metabolic networks. iScience, 2018, vol. 7, p. 212-229. - https://doi.org/10.1016/j.isci.2018.07.022

TÜREI, Dénes, KORCSMÁROS, Tamás, et SAEZ-RODRIGUEZ, Julio. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nature methods, 2016, vol. 13, no 12, p. 966. - https://doi.org/10.1038/nmeth.4077

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

SCHUBERT, Michael, KLINGER, Bertram, KLÜNEMANN, Martina, et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nature communications, 2018, vol. 9, no 1, p. 1-11. - https://doi.org/10.1038/s41467-017-02391-6

GARCIA-ALONSO, Luz, HOLLAND, Christian H., IBRAHIM, Mahmoud M., et al. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome research, 2019, vol. 29, no 8, p. 1363-1375. - http://dx.doi.org/10.1101/gr.240663.118

GARCIA-ALONSO, Luz, IORIO, Francesco, MATCHAN, Angela, et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer research, 2018, vol. 78, no 3, p. 769-780 - http://dx.doi.org/10.1158/0008-5472.CAN-17-1679

DUGOURD, Aurelien et SAEZ-RODRIGUEZ, Julio. Footprint-based functional analysis of multi-omic data. Current Opinion in Systems Biology, 2019. - https://doi.org/10.1016/j.coisb.2019.04.002

YU, Isseki, MORI, Takaharu, ANDO, Tadashi, et al. Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm. Elife, 2016, vol. 5, p. e19274. - https://doi.org/10.7554/eLife.19274

MARKOWETZ, Florian. How to understand the cell by breaking it: network analysis of gene perturbation screens. PLoS computational biology, 2010, vol. 6, no 2. - https://dx.doi.org/10.1371%2Fjournal.pcbi.1000655

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

image

Sélection Signaler une erreur

TUTELLES

PARTENAIRES

Destination de la recherche

Raccourcis

Voir Aussi