Journées Bioss-Médecine-Personalisée

Informations générales

Date : 1er et 2 juillet (matin) 2019

Lieu : Salles A-B-C, Bâtiment 34, LS2N - Laboratoire des Sciences du Numérique de Nantes, Université de Nantes Faculté des Sciences et Techniques, 2 Chemin de la Houssinière, 44322 Nantes

Organisatrice : Carito GUZIOLOWSKI  

Lundi 1er Juillet

09h00 - 09h15 - Accueil - Café
09h15 - 10h00 - Maxime Folschette (LS2N, Nantes) - Search of Therapeutic Targets on the Hepatocellular Carcinoma with Database Extraction and Graph Coloring Methods.
10h00 - 10h45 - Benno Schwikowski (Institut Pasteur, Paris) - Machine learning for the discovery of new physiology from omics data.
10h45 - 11h15 - Pause
11h15 - 12h00 - Mohamed Elati (U908, Lille) - TBA.
12h00 - 12h45 - Herve Isambert (Institut Curie, Paris) - Learning clinical networks from medical records based on information estimates in mixed-type data
12h45 - 14h00 - Pause déjeuner

14h00 - 14h45 - Denis Thieffry (IBENS, Paris) - Logical modelling, cell differentiation, dynamical simulations.
14h45 - 15h30 - Samuel Chaffron (LS2N, Nantes) - Human gut microbiome co-activity networks in heath and disease.
15h30 - 15h45 - Pause
15h45 - 16h30 - Laurence Calzone (Institut Curie, Paris) - Une méthodologie de personalisation des modèles Booléens pour tester des inhibiteurs, simples ou doubles, avec des réponses qui varient selon les profils de patients.
16h30 - 17h15 - Celia Biane (IRISA, Rennes) - Different approaches for the identification of perturbations in Boolean networks

Mardi 2 Juillet

09h00 - 09h15 - Accueil - Café
09h15 - 10h00 - Loic Paulevé (LaBRI, Bordeaux) - Théorie et cas pratiques pour l'apprentissage de réseaux booléens pour la différenciation cellulaire.
10h00 - 10h45 - Dimitri Meistermann (CRTI, LS2N, Nantes) - The limit of cell specification concept: a lesson from scRNA-Seq on early human development.
10h45 - 11h15 - Pause
11h15 - 12h00 - Vera Pancaldi (CRCT, Toulouse) - Quantification of tumour-infiltrating immune cells and beyond: modelling of cellular interactions in the tumour micro-environment.
12h00 - 12h45 - Diana Mateus (LS2N, Nantes) - Prognosis Prediction of Myeloma Patients with Random Survival Forests.


Diana Mateus - Prognosis Prediction of Myeloma Patients with Random Survival Forests
Multiple myeloma (MM) is a bone marrow cancer that accounts for 10\% of all hematological malignancies. FDG PET Quantitative imaging has great importance for its treatment protocol guidance. In this study, we aim to develop a computer-assisted method based on PET imaging features towards assisting personalized diagnosis and treatment decisions for MM patients. We consider texture-based (radiomics) features on top of conventional (e.g. SUVmax) and clinical biomarkers, resulting in a large input/feature vector. Our proposed model relies on a two-stage Random Survival Forest (RFS) for both feature selection and prediction. The targeted variable for prediction is the progression-free survival(PFS), that is, the period of time until the first progression or relapse. We demonstrate the performance of the proposed approach in terms of C-index and final prognosis separation on a database of 66 patients who were part of the prospective multi-centric french IMAJEM study. Our results confirm the predictive value of radiomics for MM patients. Indeed, quantitative/heterogeneity image-based features reduce the error of the predicted progression.

Celia Biane - Different approaches for the identification of perturbations in Boolean networks
Boolean networks are discrete dynamical systems that are increasingly used in the field of systems biology to understand how complex cellular behaviors (phenotypes) emerge from the interaction of their molecular components. In this context, the interacting elements of the network represent diverse molecules whose local Boolean state is influenced by the state of other elements of the network, and the asymptotic states of the network represent the phenotype. During the last few years, different modelling and algorithmic approaches have been proposed for the identification of sets of local perturbations leading to a goal asymptotic behavior. During this presentation, I will propose different criteria of comparison of these approaches, show their application on a published model of bladder cancer and discuss their interpretation in the context of personalized/precision medicine.