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Lecture and HADDOCK tutorial as part of the CECAM BImMS 2019 meeting

Saturday, October 5, 2019 - 09:00 to Sunday, October 6, 2019 - 13:00

The school is co-organized with the COST Action MuTaLig. The COST action will support 30 selected applicants to partially cover their travel and living expenses. The selection of the COST supported trainees will be carried out by the a Scientific Commission composed by four members of the COST Action, involved in computational research activities. The grant will be sent only after following the training school, according to the rules of the COST Vademecum (Part B).

More details in the web page:

NGSchool2019: Machine Learning for Biomedicine

Thursday, October 24, 2019 - 09:00 to Thursday, October 31, 2019 - 18:00

Advances in biological and medical technologies drive continuous generation of large amounts of biomedical Big Data. European Nucleotide Archive stores 260 million sequences comprising 339 trillion nucleotidesThis will double in less than 3 years if the current rate of growth is sustained! Given the exponential progress in sequencing technology the increase will only get steeper, entailing an intensified demand for experts in NGS data analysis. Big Data requires applying new solution to leverage its potential. Machine Learning (ML) is the answer to the increased complexity of research problems in science, industry and in everyday life. It is our conviction that knowledge of the ML techniques is a crucial skill every data scientist should acquire throughout their training.

For above reasons, #NGSchool2019: Machine Learning for Biomedicine will be focused on Machine Learning (ML) and its application in Bioinformatics & NGS Data Analysis as well as personalised medicine. We will cover the following subjects:

  • Introduction to Linux, programming (R and Python) and statistics

  • Tools for efficient and reproducible research

  • Modern  and  libraries/packages for biomedical data science

  • Deep learning in long read sequencing data analysis

  • Statistical and probabilistic analysis of biomedical data

  • Integration of genomics data using ML for understanding gene regulation in its three dimensional context

  • Quality control and typical mistakes of a beginner ML user