Interactive machine learning from multiple biodata sources

  • Sundin, Iiris (Project Member)
  • Kaski, Samuel (Principal investigator)
  • Afrabandpey, Homayun (Project Member)
  • Chen, Yi (Project Member)
  • Aushev, Alexander (Project Member)
  • Honkamaa, Joel (Project Member)
  • Blomstedt, Paul (Project Member)
  • Hegde, Pashupati (Project Member)
  • Siren, Jukka (Project Member)
  • Pesonen, Henri (Project Member)
  • Kangas, Juho-Kustaa (Project Member)
  • Qin, Xiangju (Project Member)
  • Shen, Zheyang (Project Member)
  • Peltola, Tomi (Project Member)
  • Celikok, Mustafa Mert (Project Member)
  • Daee, Pedram (Project Member)
  • Eranti, Pradeep (Project Member)
  • Jälkö, Joonas (Project Member)
  • Reinvall, Jaakko (Project Member)

Project Details


Precision medicine, which is tailored to individual patients, brings a difficult challenge to data analysis methods: The goal is to learn to predict effectiveness of treatments using data with extremely small, even equal to one patient, sample size but an extremely large number of variables. Additional data exist but their relevance is unknown, access may be restricted due to privacy, and the complex models required for good predictivity are computationally heavy. Prof. Kaski develops computational, so-called machine learning methods for analysing multiple data sources, taking into account tacit knowledge of domain experts with interactive modelling. Along with the increasing data-intensivity, similar problems have become very general in other fields of science and services, starting from information retrieval.
Effective start/end date01/01/201631/12/2018

Collaborative partners

  • Aalto University (lead)
  • SA: Research funding (other) (Project partner)
  • Suomen Akatemia (Project partner)

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being


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