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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
H2020Докторантска мрежа2019–2024

MLFPM2018 · Machine Learning Frontiers in Precision Medicine

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2019-01-01 → 2024-03-31
Финансиране от ЕС
3 618 387 €
Участници
16
Схема
MSCA-ITN

Линиите свързват координатора с партньорите.

Накратко на български

Алгоритми за машинно обучение се разработват за анализ на огромни масиви от данни, като например генетични последователности и медицинска история. Това помага за създаването на персонализирано лечение и превенция, съобразени с индивидуалните особености на всеки пациент.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Machine Learning Frontiers in Precision Medicine

Healthcare is entering the digital era: More and more patient data, from the molecular level of genome sequences to the level of image phenotypes and health history, are available in digital form. Exploring this big health data promises to reveal new insights into disease mechanisms and therapy outcomes. Ultimately, the goal is to exploit these insights for Precision Medicine, which hopes to offer personalized preventive care and therapy selection for each patient. A technology with transformational potential in analysing this health data is Machine Learning. Machine Learning strives to discover new knowledge in form of statistical dependencies in large datasets. Mining health data is, however, not a simple direct application of established machine learning techniques. On the contrary, the emerging population-scale and ultra-high dimensionality of health data creates the need to develop Machine Learning algorithms that can successfully operate at this scale. Overcoming these frontiers in Machine Learning is key to making the vision of Precision Medicine a reality. To meet this challenge, Europe urgently needs a new generation of scientists with knowledge in both machine learning and in health data analysis, who are extremely rare at a global scale. The goal of the “Machine Learning Frontiers in Precision Medicine” ETN is to close this gap, by bringing together leading European research institutes in Machine Learning and Statistical Genetics, both from the private and public sector, to train 14 early stage researchers. These scientists will help to shape the future of this important topic and increase Europe’s competitiveness in this domain, which will have severe academic and industrial impact in the future and has the potential to shape the healthcare and high tech sector in Europe in the 21st century.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Healthcare is entering the digital era: More and more patient data, from the molecular level of genome sequences to the level of image phenotypes and health history, are available in digital form. Exploring this big health data promises to reveal new insights into disease mechanisms and therapy outcomes. Ultimately, the goal is to exploit these insights for Precision Medicine, which hopes to offer personalized preventive care and therapy selection for each patient. A technology with transformational potential in analysing this health data is Machine Learning. Machine Learning strives to discover new knowledge in form of statistical dependencies in large datasets. Mining health data is, however, not a simple direct application of established machine learning techniques. On the contrary, the emerging population-scale and ultra-high dimensionality of health data creates the need to develop Machine Learning algorithms that can successfully operate at this scale. Overcoming these frontiers in Machine Learning is key to making the vision of Precision Medicine a reality.To meet this challenge, Europe urgently needs a new generation of scientists with knowledge in both machine learning and in health data analysis, who are extremely rare at a global scale. Our ETN’s goal is to close this gap, by bringing together leading European research institutes in Machine Learning and Statistical Genetics, both from the private and public sector, to train 14 early stage researchers. These scientists will help to shape the future of this important topic and increase Europe’s competitiveness in this domain, which will have severe academic and industrial impact in the future and has the potential to shape the healthcare and high tech sector in Europe in the 21st century.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз