English
Part 2 - Dynamic logic models complement machine learning for personalized medicine
Multi angle
Auteurs : Saez-Rodriguez, Julio (Auteur de la Conférence)
CIRM (Editeur )
92-08 - Computational methods
92B05 - General biology and biomathematics
92C42 - Systems biology, networks
92-10 - Mathematical modeling or simulation for problems pertaining to biology
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 : 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
92-08 - Computational methods
92B05 - General biology and biomathematics
92C42 - Systems biology, networks
92-10 - Mathematical modeling or simulation for problems pertaining to biology
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : 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 
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Informations sur la Rencontre
Nom de la rencontre : Thematic Month Week 5: Networks and Molecular Biology / Mois thématique Semaine 5 : Réseaux et biologie moléculaireOrganisateurs 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.19622103Citer 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
- [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.
- [Multi angle] Part 1 - Networks of prior knowledge as frames to understand complex biological data / Auteur de la Conférence Saez-Rodriguez, Julio.
Bibliographie
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- 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
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- 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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