Project description
Harnessing statistical analyses to improve multiple myeloma outcomes
Multiple myeloma is a type of bone marrow cancer affecting white blood cells called plasma cells. These cells help the body fight infections by making antibodies that recognise and attack invaders. In multiple myeloma, cancer cells that produce abnormal proteins accumulate in the bone marrow, destroying bone and displacing healthy blood cells. The cancer can affect multiple areas of the body, including the spine, skull, pelvis and ribs, hence the term 'multiple myeloma'. The relapse rate is 100 % – it is not curable but can go into remission. However, treatments are available. The EU-funded LIMORD project is developing a statistical tool that will be incorporated into a software package for clinical use, enabling more accurate patient classification, earlier relapse detection and better prognosis estimation. The tool will support personalised medicine and improve patient outcomes.
Objective
The goal of my project is to propose a novel statistical tool allowing patient classification, earlier relapse detection and better prognosis estimation in order to move forward into personalized medicine in Multiple Myeloma. To this aim, I will develop new statistical models and computational schemes to incorporate large follow-up omics datasets in a decision framework. As a statistician coming from theoretical mathematics, this project will provide me a unique opportunity to acquire new knowledge in biology and new supervision skills in order to translate theoretical mathematical results into real added value in the way we treat patients.
The first challenge I will address is the development of statistical methods based on Variational Auto-Encoders to integrate multiple omics data-type at multiple time-points. My model will have to be flexible enough to allow for missing data (for instance a full omic dataset missing at a given time point due to experiment failure) and to accommodate for data acquired in an online manner. The second challenge I will address is the development of quality metrics and analysis methods for direct RNA sequencing data from patient samples. The third challenge I will address concerns the numerical inference difficulties of Partially Observable Markov Decision Processes when the dimension of the data increases. Approximation strategies will be investigated to make use of the high-dimensional, heterogeneous biological data in a relapse detection framework. Finally, I will develop a software package incorporating our results intended to help clinicians take the optimal decision when treating their patients.
An important aspect of my project is to integrate it both to a biological laboratory in Australia and a mathematical group in France, together with a collaboration with clinicians in a French hospital, hence I will carry out the entire process of designing the statistical tool and its software package for a concrete use in the clinic.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences software
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
- medical and health sciences health sciences personalized medicine
- natural sciences mathematics applied mathematics statistics and probability
- natural sciences biological sciences genetics RNA
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2019
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
75794 PARIS
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.