LIMORD · Longitudinal Integrative Models for Online Relapse Detection
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2022-08-01 → 2024-07-31
- Финансиране от ЕС
- 125 645 €
- Участници
- 2
- Схема
- MSCA-IF
Линиите свързват координатора с партньорите.
Накратко на български
Данните от клинични изследвания и генетичното секвениране при пациенти с множествен миелом се анализират, за да се открие рецидив на болестта. Това помага за по-ранно откриване на симптомите и по-добър подбор на подходящото лечение за всяка група пациенти.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Longitudinal Integrative Models for Online Relapse Detection
The project is focused on exploiting longitudinal and heterogeneous patient data in order to detect their relapse. In Multiple Myeloma (MM), detecting relapse remains a difficult task, with about a third of detections evaluated from painful symptoms. Clinical factors such as translocation t(4;14) or deletion of chromosome 17p are known to impact prognosis but there remains a large heterogeneity and the underlying regulation mechanisms are poorly understood. Personalized medicine is a practice of medicine that has started emerging with the rise of genomics technologies in the early 2000s, and which aims at tailoring medical decisions to the patient’s predicted response. It is often misinterpreted as implying that unique treatments can be designed for each individual. If this idea represents the holy grail of the field, the current status rather consists in using diagnostic tests to determine which medical treatments will work best for each patient, by stratifying diseases into subgroups. This project is an attempt to integrate high-dimensional and longitudinal sequencing and clinical data in order to provide earlier relapse detections, more relevant patient classifications, and get insight into the complex mechanisms driving disease progression and reaction to treatment.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
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.
Оригинален текст от CORDIS (на английски).
Участници
Връзки
- Виж в CORDIS
- DOI: 10.3030/890462
- https://www.alice-cleynen.menopresto.net/longitudinal-integrative-models-for-online-relapse-detection-limord/
Данни: CORDIS, © Европейски съюз