The diversity across tumors from different patients and even across cancer cells from the same patient makes the picture very complex. The idea of ‘personalized’ or ‘precision’ medicine has been suggested, aiming to find tailored treatment regimen for each patient according to the individual genetic background and tumor molecular profile. This attempt is achievable thanks to sufficient molecular characterization of cancers accumulated using high-throughput technologies and advanced imaging technologies. However, despite availability of cancer multi-scale data, they are not fully exploited to provide the clue on deregulated mechanisms that would guide better patients stratification and to specific treatment in cancer.
This course is a FEBS-supported event.
Please note that due to the current pandemic situation and strict regulations regarding face-to-face meetings, this will be a REAL-TIME VIRTUAL COURSE (Paris Time, GMT+1).
Inscription deadline postponed to September 10th, 2021.
The objective of the course is to promote better integration of computational approaches into biological and clinical labs and to clinics. We aim to help participants to improve interpretation and use of multi-scale data that nowadays are accumulated in any biological or medical lab.
This year, the course will particularly focus on application of Machine Learning (ML) approaches for multi-omics data analysis in cancer research and benefits for clinics.
We will review current methods and tools for the analysis and interpretation of big data, along with concrete applications related to cancer. In particular, we will emphasize the role of ML methods for understanding the heterogeneity of tumor and applications in personalized treatment schemes development in precision medicine applications by leveraging multi-omics and machine learning approaches.
We invited leading speakers from different fields in cancer systems biology, especially from the field of big data and Machine Learning (ML) in cancer research and in clinics. The invited speakers will expose various approaches for omics, imaging, clinical data analysis and interpretation, combining signalling networks together with multi-scale data and associating it to clinical data. In addition, drug sensitivity prediction algorithms, biomarkers and cancer drivers identification; patient stratification approaches; application of mathematical modelling and image analysis in cancer with focus on ML approaches for integrating multi-omics data analysis will be demonstrated.
This course is supported by the Federation of European Biochemical Societies (FEBS). One of FEBS’ core objectives is the publication of high-quality journals by scientists for scientists. Check HERE for more information about FEBS Journals.
Round table for career development
In addition to attendance of the online lectures, the participants are expected to take part in the following virtual events:
Pre-selected PhD and post-doc participants will be asked to:
Pre-selected Master students participants will be assigned seminal articles in the field. They will be asked to prepare, submit their presentations and present the assigned articles during virtual journal club sessions.
The course will be validated by 4 ECTS for Master and PhD students.
Evaluation of students (by scientific committee)
The course will be validated by 4 ECTS for Master and PhD students.
Attendance of the virtual course will be monitored in a daily basis, using online forms. Course certificates will be issued and sent to the participants at the end of the course.
Julio SAEZ-RODRIGUEZ - DE
Magnus RATTRAY - UK
Emmanuel BARILLOT - FR
Anaïs BAUDOT - FR
Olivier AYRAULT - FR
Nathalie VIALANEIX - FR
Carl HERRMANN - DE
Andrei ZINOVYEV - FR
Kay NIESELT - DE
Stéphanie ALLASSONNIERE - FR
Samuel KASKI - FI
Irène BUVAT - FR
Asmund FLOBAK - NO
Joakim LUNDEBERG - SE
Pierre FILLARD - FR
Nikos PARAGIOS - FR
Thomas WALTER - FR
Jean-Philippe VERT - FR
Chloé-Agathe AZENCOTT - FR
Lodewyk WESSELS – NL
Joaquin DOPAZO - ES
Laure FOURNIER - FR
Yvan SAEYS – BE
This list of speakers may still change
Inna KUPERSTEIN - Institut Curie, FR
Emmanuel BARILLOT - Institut Curie, FR
Stéphanie ALLASSONNIERE - Université Paris Descartes, France , Ecole Polytechnique, France
Kay NIESELT - University of Tübingen, DE
Denis THIEFFRY - IBENS - ENS - FR
Mathur PALLAVI - Institut Curie, FR
Aafrin PETTIWALA - Institut Curie, FR
Inna KUPERSTEIN - Institut Curie, FR
Emmanuel BARILLOT - Institut Curie, FR
Chloé-Agathe Azencott CBIO - MINES - ParisTech – FR
Thomas WALTER CBIO - MINES - ParisTech – FR
Stéphanie ALLASSONNIERE - Université Paris Descartes, France , Ecole Polytechnique, France
Kay NIESELT - University of Tübingen, DE
Denis THIEFFRY - IBENS - ENS - FR
Laurence CALZONE - Institut Curie, FR
Andrei ZINOVYEV - Institut Curie, FR
This is a real-time virtual course. The registration for students and post-doc is free-of-charge. Registration is mandatory for all categories of attendees.
Upon the registration and following the selection procedure, the course participants will be provided with access to:
The real-time online course can accommodate up to 150 Master 2, PhD students and post-docs.
In addition, we expect attendees among researchers, physicians and pharma companies representatives interested to learn about systems biology impact into cancer research and treatment.
No previous experience in programming and modelling methods or deep knowledge in systems biology is required. Talks will be constructed in a didactic manner, introducing the audience to the basics on systems biology approaches and applications for cancer research.
Selections for participation in the course are based on your application documents, as listed below :
1 - Motivation satement explaining objectives to attend the course (max. 2000 caracters )
2 - Research project Abstract or Description of a scientifi topic in the form of abstract (max 200 caracters)
3 - 1 PDF file compile the following CV (max. 1 page); Description of scientific topic in a form of abstract (max. 1 page)
4 - 1 PDF file - Recommendation letter from at least 1 reference contact
Note: if your reference(s) wishes to communicate directly with the course's scientific committee, the recommendation letter(s) can be sent to BEFORE the registration deadline.
