ESHG Symposium “Machine Learning for Personalized Medicine”, Barcelona 2016

ESHG Symposium “Machine Learning for Personalized Medicine”, Barcelona 2016

7 Lectures · May 19, 2016

About

Machine learning is a powerful tool to analyze large, high-dimensional biomedical data sets (e.g. genetic, proteomic, or transcriptomic data). The objective is to find patterns in the deep sea of data that are significantly related to the development or progression of diseases or play a role in individual drug responses. Ultimately, machine learning algorithms are developed to provide physicians and other health professionals with clinical decision support.

At ESHG Symposium 2016 we bring together experts from this highly interdisciplinary field: human geneticists, bioinformaticians and machine learners will discuss recent research results and benefit from each others expertise.

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01:36:52

Systems genetics with graphical Markov models

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Lecture
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Unlocking the potential of large prospective biobank cohorts for -omics data ana...

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Integrative and quantitative analysis of disease mutations in the RAS-RAF-MEK-ER...

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A network biology approach to epigenetic regulation

Alfonso Valencia

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Identifying drug-targetable key drivers of disease

Lude Franke

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Removing unwanted variation in machine learning for personalized medicine

Terry Speed

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Development of methods for patient group stratification and tailored medical int...

Daniel Urda Muñoz

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Lecture