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First Rank Performance in the sbv IMPROVER Species Translation Challenge

31 oktober 2013

A team of researchers including Michael Biehl, Prof. of Computer Science at the Johann Bernoulli Institute, University of Groningen, has taken first place honors in the Species Translation Challenge organized by IBM Research and Philip Morris International (PMI) R&D.

Biehl teamed up in an interdisciplinary and transatlantic collaboration with Prof. Gyan Bhanot from Rutgers University, Physics and Molecular Biology and Biochemistry, and post-doctoral researchers Dr. Adel Dayarian and Dr. Sahand Hormoz from the Kavli Institute for Theoretical Physics at UC Santa Barbara.

The team reported their findings at the sbv IMPROVER Symposium held in Athens, Greece, October 29-31 2013. sbv (systems biology verification) IMPROVER is a collaborative project designed to enable scientists to learn about and contribute to the development of new methods for verification of scientific data and results. The project is funded by PMI. This challenge focused on understanding the limits of rodent models for human biology. Teams were scored using a gold standard where predictions were compared to unreleased experimental data.

The team named AMG formed by Bhanot, Biehl, Dayarian and Hormoz achieved first rank performance in three of the four sub-challenges. A research grant of US$ 20000 is awarded per sub-challenge, which is divided among three best performing teams for one of the sub-challenges while for two other sub-challenges, AMG was the only rank one performing team.

Open to academic and industrial researchers around the world, the challenge called on participants to design the most effective computational method for inferring cellular response in humans, based on high-throughput biological data in rats. While studies in rodents have been instrumental in understanding a range of human ailments, translating those findings to humans remains a fundamental challenge in biomedical research.

As an integral part of this interdisciplinary effort, the design and application of machine learning techniques in the analysis of gene expression and other biological data was instrumental in achieving accurate predictions. Michael Biehl’s recent research interests focus on the development of efficient machine learning methods and their application in the bio-medical domain.


Michael Biehl is available at Information about his research interests and previous publications are available at

For further information about the sbv IMPROVER project and challenges visit

Laatst gewijzigd:31 mei 2018 16:10

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