Machine Learning for Biomedicine

  • Location : Białobrzegi, Poland
  • Date : October 24 - 31, 2019
  • Attendees : 35 participants, 11 speakers, 8 organisers

NGSchool2019: Machine Learning for Biomedicine was held on 24-31 October in Białobrzegi, Poland, and was focused on Machine Learning (ML) and its application in Bioinformatics & NGS Data Analysis as well as personalised medicine.

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 nucleotides. This 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.

We invited ~60 participants (40 students and 20 speakers). You can find all details about this edition in Book of abstracts. Materials from the school and video recordings of keynote lectures are available online.

We covered the following subjects:

  • introduction to Linux, programming (R and Python) and statistics
  • tools for efficient and reproducible research
  • modern R and Python 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

Program & speakers

This year we hosted introductory sessions, lectures, workshops and hackathons. First two days included introductory sessions covering Linux, programming (R/Python) and statistics, in order to make sure all participants are at the same level and have necessary software and packages installed and configured. Days 3-5 were filled with workshops and lectures on Machine Learning related topics given by the invited speakers. Days 6-8 consisted of 6 hackathon projects (~6 participants per each project). Every project was be guided by the mentor. On the last day we concluded all hackathon projects with presentations, discussion and summary.

Social activities

Similarly to previous years, we made sure there is plenty of fun beside hard working during workshops :)