Photo

Markus Heinonen, Postdoc

Postdoc at

Université d’Evry-Val d’Essonne
Institute: IBISC
Group: Statistical Learning, Modeling and Data Integration - Application to Systems Biology (AMIS)

Offices: room 215, building IGBGI of IBISC
LRI building at Orsay-Saclay
email: markus.heinonen@ibisc.fr
mobile: +358 44 294 2600

Mailing address

Université d’Evry-Val d’Essonne
IBISC EA 4526, Bât. IBGBI, 23 bd de France
91037 cedex Evry, France


I work as a post-doc at Prof. Florence d'Alché-Buc's groups: Statistical learning, modeling and data integration - application to systems biology (AMIS) and Algorithms and Models for Integrative Biology (AMIB) at University of Evry and Saclay Campus in Paris researching kernel methods for gene regulation networks related to irradiation.

I completed my PhD studies at Prof. Juho Rousu's group: "Kernel Machines, Pattern Analysis and Computational Biology" (KEPACO). My PhD thesis is "Computational Methods for Small molecules" (2012).

I am a member of the board of the Finnish Society for Bioinformatics.

Research

My research focus is to apply machine learning on bioinformatics problems, especially regarding metabolism and small molecules.

I develop algorithms, methods and models for bioinformatics problems. The main research problems are developing models and representations for biochemical reactions, predicting properties of small molecules, and predicting the structural properties of compounds and fragments through tandem mass spectrometry. Our models embrace the high dimensionality of tackling 3D objects such as molecules through novel kernel methods.

Refereed publications

Markus Heinonen, Huibin Shen, Nicola Zamboni and Juho Rousu
Metabolite identification and fingerprint prediction via machine learning
Bioinformatics, 28(18):2333-41, 2012
[ abstract | preprint PDF ]

First application of machine learning to identify metabolites based on MS/MS data. We use probability product kernel over mass spectral features to learn a mapping between mass spectrum and binary structural properties of the unknown metabolite. We show that the properties can be used to query the unknown structure from e.g. PubChem.

Markus Heinonen, Niko Välimäki, Veli Mäkinen and Juho Rousu
Efficient path kernels for reaction function prediction
In Proceedings of 3rd International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS), 2012, pages 202-207
[ abstract | preprint PDF ]

We introduce first feasible path-based graph kernel. The main contribution is to apply a compressed string index to store millions of paths efficiently. We utilize the path kernel to predict chemical reaction function (EC class) over reaction graphs.

Markus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen and Juho Rousu
Computing atom mappings for biochemical reactions without subgraphs isomorphism
Journal of Computational Biology 18(1):43-58, 2011
[ abstract | preprint PDF ]
[ KEGG 01/2009 atommappings | bin + src ]

We study the problem of mapping the atoms between reactants and products in a chemical reaction. We introduce the first definition of optimality of such mappings through graph edit distance. An A* algorithm is applied to compute the optimal mappings of KEGG reactions. We also introduce atom level descriptors through a message passing algorithm.

Hongyu Su, Markus Heinonen and Juho Rousu
Structured output prediction of anti-cancer drug activity
Proceedings of PRIB 2010
[ abstract | PDF ]

We utilize MMCRF for structured output prediction on small molecules for effectiveness against 59 cancer cell lines. Structured prediction outperforms individual SVM's clearly. However, the structure of the outputs seems to have little effect on performance.

Hongyu Su, Markus Heinonen and Juho Rousu
Multilabel Classification of Drug-like Molecules via Max-margin Conditional Random Fields
Proceedings of PGM 2010
[ PDF ]

Markus Heinonen, Ari Rantanen, Taneli Mielikäinen, Juha Kokkonen, Jari Kiuru, Raimo Ketola and Juho Rousu
FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data
Rapid Communications in Mass Spectrometry 22:3043-3052, 2008
[ abstract | PDF ]

We introduce software for identifying product ions from MS/MS data. The method outperforms rule-based methods in our dataset of amino acids and sugarphosphates.

Markus Heinonen, Ari Rantanen, Taneli Mielikäinen, Esa Pitkänen, Juha Kokkonen and Juho Rousu
Ab initio prediction of molecular fragments from tandem mass spectrometry data
Proceedings of GCB 2006, Vol P-83:40-53
[ PDF ]

We present a combinatorial algorithm for searching of plausible fragment structures for product ion peaks, based on a bond energy scoring function. We also introduce a mixed integer linear programming algorithm for choosing an optimal fragmentation tree.

Posters

Suvi Heinonen, Markus Heinonen and Emilia Koivisto
Full waveform forward seismic modeling of geologically complex environment: Comparison of simulated and field seismic data
To be presented at EGU 2012
[ abstract PDF ]

We experiment with full seismic forward simulation modeling as a method to find approximations for seismic models.

Theses

Ph.D. Thesis: Computational methods for small molecules
University of Helsinki, Department of Computer Science, 2012
[ e-thesis | PDF ]

M.Sc. Thesis: (in finnish) Algoritminen tytärionien tunnistus massaspektrometriadatasta (Algorithmic identification of daughter ions in mass spectrometry data)
University of Helsinki, Department of Computer Science, 2007
[ PDF ]

Proceedings

Editors/organizers: Masanori Arita, Markus Heinonen and Juho Rousu
Mass Spectrometry Informatics in Systems Biology (MSiB 2010)
Abstracts of the Workshop, October 28-29, 2010, Helsinki, Finland
[ abstracts ]

Teaching

Below is a list of courses where I have been a teacher / teacher's assistant at the University of Helsinki