Tommi Suvitaival

Tommi Suvitaival

Systems Medicine

Phone: +45 30 91 34 01



Research Area
I am a data scientist at Steno Systems Medicine and my job is to analyse large biomedical data sets in connection to health. My work involves computational fusion and modelling of measurement data from multiple sources -- such as different measurement locations and devices -- and from multiple conditions -- such as different medical statuses and measurement times. I have a background in machine learning and computational biology, particularly in unsupervised Bayesian multi-source modelling (see thesis).

Ongoing Projects
EU FP7 METSY - Neuroimaging Platform for Characterisation of Metabolic Co-Morbidities in Psychotic Disorders

2014 Doctor of Science (Technology) in Information and Computer Science, Aalto University
2009 Master of Science (Technology) in Bioinformatics, Helsinki University of Technology

Latest Positions
Post-Doctoral Researcher at Steno Diabetes Center (2015-)
Visiting Researcher at School of Computing Science, University of Glasgow (2012)
Doctoral Student at Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University (2009-2014)

Presentations in the Past 3 Years
Bayesian Multi-Way Models for Data Translation in Computational Biology. Doctoral Dissertation at Aalto University, November 2014.

Stronger Findings for Metabolomics Through Bayesian Modeling of Multiple Peaks and Compound Correlations. Conference presentation at the 13th European Conference on Computational Biology (ECCB’14), September 2014.

Cross-Organism Prediction of Drug Hepatotoxicity by Sparse Group Factor Analysis. Conference presentation at the 12th Annual International Conference on Critical Assessment of Massive Data Analysis (CAMDA’13), July 2013.

Predicting Malt Quality from Barley Gene Expression. Poster presentation at the 34th International Congress of the European Brewery Convention (EBC’13), May 2013.

List of Publications
Stronger findings for metabolomics through Bayesian modeling of multiple peaks and compound correlations
Tommi Suvitaival, Simon Rogers, and Samuel Kaski
Bioinformatics, 30(17):i461-i467, 2014 (ECCB'14).

Stronger findings from mass spectral data through multi-peak modeling
Tommi Suvitaival, Simon Rogers, and Samuel Kaski
BMC Bioinformatics, 15:208, 2014.

Cross-organism toxicogenomics with group factor analysis
Tommi Suvitaival, Juuso A. Parkkinen, Seppo Virtanen, and Samuel Kaski
Systems Biomedicine, 2:e29291, 2014.

Cross-species translation of multi-way biomarkers
Tommi Suvitaival, Ilkka Huopaniemi, Matej Orešič, Samuel Kaski
In Timo Honkela et al., editors, Artificial Neural Networks and Machine Learning - ICANN 2011, volume 6791 of Lecture Notes in Computer Science, pages 209-216. Springer Berlin / Heidelberg, 2011.

Graphical multi-way models
Ilkka Huopaniemi, Tommi Suvitaival, Matej Orešič, Samuel Kaski
In José Balcázar et al., editors, Machine Learning and Knowledge Discovery in Databases, volume 6321 of Lecture Notes in Computer Science (ECML PKDD 2010), pages 538-553. Springer Berlin / Heidelberg.

Multivariate multi-way analysis of multi-source data
Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešič, Samuel Kaski
Bioinformatics, 26(12):i391-i398, (ISMB) 2010.

Two-way analysis of high-dimensional collinear data
Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešič, Samuel Kaski
Data Mining and Knowledge Discovery, 19(2):261-276, (ECML PKDD) 2009.

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Sidst opdateret 12-01-2017