Multi angle

H 1 Network approaches for personalized medicine

Auteurs : Martinez-Rodriguez, Maria (Auteur de la Conférence)
CIRM (Editeur )

    Loading the player...

    Résumé : In this talk, I will present current activities of the Computational Systems Biology group at IBM Research, Zurich, focused on the inference and exploitation of networks of molecular interactions. Focusing first on the problem of network inference, a long-standing challenge for which many methods have been proposed, I will discuss how no single inference method performs optimally across all data sets.
    However, a Wisdom of the Crowds approach based on the integration of multiple inference methods can increase the robustness and high performance of the inferred networks. To that aim, we have developed COSIFER, a web-based platform that enables the inference of molecular networks using different approaches and consensus strategies. Next, I will introduce INtERAcT, an approach to extract information about molecular interactions from a text corpus in a completely unsupervised manner. INtERAcT exploits word embeddings, a state-of-the-art technology for language modelling based on deep learning that does not require text labeling for training or domain-specific knowledge, and hence, can be easily applied to different scientific domains.
    Moving into the applications, I will explain how prior information about the molecular interactions in a cell can be encoded in a network, which can be further used for gene prioritization. Such strategy is exploited by NetBiTE with the goal of identifying anti-cancer drug sensitivity biomarkers. Finally, I will discuss how a probabilistic application of network dynamics can enable the reconstruction of the cell-signaling dynamics using single-cell omics.

    Keywords : biological modeling; data analysis

    Codes MSC :
    92-10 - Mathematical modeling or simulation for problems pertaining to biology
    68T09 - Computational aspects of data analysis and big data

    Ressources complémentaires :

      Informations sur la Vidéo

      Langue : Anglais
      Date de publication : 23/03/2020
      Date de captation : 03/03/2020
      Sous collection : Research talks
      Format : MP4
      arXiv category : Computer Science ; Quantitative Biology
      Domaine : Probability & Statistics ; Analysis and its Applications ; Computer Science
      Durée : 01:30:31
      Audience : Chercheurs ; Doctorants , Post - Doctorants
      Download : https://videos.cirm-math.fr/2020-03-03_Rodriguez-Martinez.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

    Citation Data

    DOI : 10.24350/CIRM.V.19620003
    Cite this video as: Martinez-Rodriguez, Maria (2020). Network approaches for personalized medicine. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19620003
    URI : http://dx.doi.org/10.24350/CIRM.V.19620003

    Voir aussi


    1. MANICA, Matteo, MATHIS, Roland, CADOW, Joris, et al. Context-specific interaction networks from vector representation of words. Nature Machine Intelligence, 2019, vol. 1, no 4, p. 181-190. - https://doi.org/10.1038/s42256-019-0036-1

    2. OSKOOEI, Ali, MANICA, Matteo, MATHIS, Roland, et al. Network-based biased tree ensembles (NetBiTE) for drug sensitivity prediction and drug sensitivity biomarker identification in cancer. Scientific reports, 2019, vol. 9, no 1, p. 1-13. - https://doi.org/10.1038/s41598-019-52093-w

    3. KUMAR, Sunil, LUN, Xiao-Kang, BODENMILLER, Bernd, et al. Stabilized Reconstruction of Signaling networks from Single-cell cue-Response Data. Scientific Reports, 2020, vol. 10, no 1, p. 1-9. - https://doi.org/10.1038/s41598-019-56444-5

Imagette Video

Titres de périodiques et e-books électroniques (Depuis le CIRM)

Ressources Electroniques

Books & Print journals

Recherche avancée