Topic outline

  • General

    Machine learning is one of the cornerstone technologies in bioinformatics, used in numerous tools and applications. This research-based course probes the state of the art in selected machine/deep learning methods and applications, through introductory lectures and project work. 

    In particular, the course focuses on applications with small molecules such as drugs or metabolites: small molecule identification, interaction and function prediction, and drug design.

    Time and Place

    Fridays 12-14, March 3 - June 2, Seminar room T3 (CS Building, 2nd foor)

    Teachers

    Juho Rousu (juho.rousu@aalto.fi) and Vikas Garg (vikas.garg@aalto.fi).

    Intended audience and prerequisite knowledge

    The course is mainly targeted to MSc and PhD students in bioinformatics, machine learning and data science.

    The prerequisite knowledge includes:

    • Required prerequisites: Machine learning: Supervised methods or equivalent knowledge
    • Recommended background knowledge: Deep learning, bioinformatics


    Enrollment to the course

    Register in SISU and send your CV and study transcript to the teachers by email.

    Registration period: Feb 10 - Feb 24

    Completing the course

    The course is completed through the following components:

    • Attending the lectures (compulsory, 1 absence allowed)
    • Project work (in groups of ca. 3-4 students)
    • Poster presentation (in groups)
    • Oral presentation (in groups)
    • Learning diaries of guest lectures (individually)
    • Final report (in groups)



    Schedules


    Registration period: Feb 10 - Feb 24

    Period IV: Feb 27- Apr 14

    March 3: Introduction lecture, Organization in groups

    March 10: Guest lecture by Anna Cichonska, Harmonic Discovery: Integration of machine learning with experimental approaches to rationally design a new generation of kinase drugs

    March 17: Guest lecture by Heli Julkunen, Nightingale Health: Metabolic blood biomarker profiling for risk prediction of various chronic diseases – evidence from 275,000 individuals in the UK Biobank. Guidance on oral presentations. 

    March 24: Q/A session for groupwork

    March 31:  Oral presentations by students:

    12:20-12:40 Group 1

    12:40- 13:00 Group 2

    13:00-13:20 Group 3

    13:20-13:40 Group 4

    13:40-14:00 Group 5

    April 3 at 14:00-15:00 (Note the earlier start time for this lecture): Guest lecture by Maria Brbic, EPFL: Machine Learning for Biomedical Discovery. Non-compulsory attendance.

    April 5: Project topic proposal deadline

    April 7: Good Friday (no session)

    April 14: Guest Lecture by Markus Heinonen, Aalto University: Generative models for molecules, Q/A session

    Period V: Apr 24 - June 2

    April 28: Guest lecture by Elena Casiraghi, Università degli Studi di Milano: Patient Similarity Networks and their integration for diagnostic/prognostic biomarker discovery, Q/A session

    May 5: Q/A session

    May12 (tentatively): Lecture by Vikas Garg, Q/A session

    May 15 at 14:15: Guest lecture by Elina Francovic-Fontaine, Laval University: “MeDIC : Metabolomic Dashboard for Interpretable Classification”. Non-compulsory attendance.

    May 19: Draft report submission. No session.

    May 26: Feedback of the draft report, Q/A session

    June  2: Poster session

    June 9 : Final report and final poster deadline. No session.