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Part 2 - Dynamic logic models complement machine learning for personalized medicine

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

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Résumé : In the second talk, I will present some of our work on this area. Our work on this area, where we have focused on transcriptomics and (phospho)proteomics to study signaling networks. Our tools range from a meta-resource of biological knowledge (Omnipath) to methods to infer pathway and transcription factor activities (PROGENy and DoRothEA, respectively) from gene expression and subsequently infer causal paths among them (CARNIVAL), to tools to infer logic models from phosphoproteomic and phenotypic data (CellNOpt and PHONEMeS). We have recently adapted these tools to single-cell data. I will illustrate their utility in cases of biomedical relevance, in particular to improve our understanding of cancer and to develop novel therapeutic opportunities. As main application I will discuss our work analysing, as a model for personalized medicine, large pharmaco-genomic screenings in cell lines. These screenings provide rich information about alterations in tumours that confer drug sensitivity or resistance. Integration of this data with prior knowledge provides biomarkers and offer hypotheses for novel combination therapies. Our own analysis as well as the results of a crowdsourcing effort (as part of a DREAM
challenge) reveals that prediction of drug efficacy from basal omics data is that discussed above is far from accurate, implying important limitations for personalised medicine. An important aspect that deserves detailed attention is the dynamics of signaling networks and how they response to perturbations such as drug treatment.
I will present how cell-specific logic models, trained with measurements upon perturbations, can provides new biomarkers and treatment opportunities not noticeable by static molecular characterisation.

Keywords : 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 : Anglais
    Date de publication : 23/03/2020
    Date de captation : 02/03/2020
    Sous collection : Research talks
    arXiv category : Machine Learning ; Quantitative Biology ; Artificial Intelligence
    Domaine : Mathematics in Science & Technology
    Format : MP4 (.mp4) - HD
    Durée : 00:42:34
    Audience : Researchers
    Download : https://videos.cirm-math.fr/2020-03-02_Saez-Rodriguez _Part2.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 ; Mossé, Brigitte ; Rémy, Elisabeth ; Tichit, Laurent ; Vignes, Matthieu
Dates : 02/03/2020 - 06/03/2020
Année de la rencontre : 2020
URL Congrès : https://conferences.cirm-math.fr/2305.html

Données de citation

DOI : 10.24350/CIRM.V.19622103
Citer cette vidéo: Saez-Rodriguez, Julio (2020). Part 2 - Dynamic logic models complement machine learning for personalized medicine. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19622103
URI : http://dx.doi.org/10.24350/CIRM.V.19622103

Voir aussi

Bibliographie

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  • IORIO, Francesco, KNIJNENBURG, Theo A., VIS, Daniel J., et al. A landscape of pharmacogenomic interactions in cancer. Cell, 2016, vol. 166, no 3, p. 740-754. - https://doi.org/10.1016/j.cell.2016.06.017

  • MENDEN, Michael P., CASALE, Francesco Paolo, STEPHAN, Johannes, et al. The germline genetic component of drug sensitivity in cancer cell lines. Nature communications, 2018, vol. 9, no 1, p. 1-8. - https://doi.org/10.1038/s41467-018-05811-3

  • MENDEN, Michael P., WANG, Dennis, MASON, Mike J., et al. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature communications, 2019, vol. 10, no 1, p. 1-17. - https://doi.org/10.1038/s41467-019-09799-2

  • 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, 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

  • 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

  • SAEZ-RODRIGUEZ, Julio, COSTELLO, James C., FRIEND, Stephen H., et al. Crowdsourcing biomedical research: leveraging communities as innovation engines. Nature Reviews Genetics, 2016, vol. 17, no 8, p. 470. - https://doi.org/10.1038/nrg.2016.69

  • CHOOBDAR, Sarvenaz, AHSEN, Mehmet E., CRAWFORD, Jake, et al. Assessment of network module identification across complex diseases. Nature methods, 2019, vol. 16, no 9, p. 843-852. - https://doi.org/10.1038/s41592-019-0509-5

  • COSTELLO, James C., HEISER, Laura M., GEORGII, Elisabeth, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nature biotechnology, 2014, vol. 32, no 12, p. 1202. - https://doi.org/10.1038/nbt.2877

  • EDUATI, Federica, DOLDÀN-MARTELLI, Victoria, KLINGER, Bertram, et al. Drug Resistance mechanisms in colorectal cancer dissected with cell type–specific dynamic logic models. Cancer research, 2017, vol. 77, no 12, p. 3364-3375. - http://dx.doi.org/10.1158/0008-5472.CAN-17-0078



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