sbv IMPROVER Launches Fourth Systems Toxicology Challenge

By Bio-IT World Staff

December 10, 2015 | sbv IMPROVER, an international scientific crowdsourcing initiative, has launched the Systems Toxicology Computational Challenge. The challenge, which is open to anyone working in computational science who is developing predictive modelling techniques, provides the unique opportunity for participants to vigorously and objectively test their methodologies. Working on blood gene expression data, participants are being challenged to derive a predictive model to distinguish current smokers from either non-smokers or former-smokers. Entries will be scored by an independent panel against a gold-standard dataset.

“sbv IMPROVER challenges are uniquely empowering,” said Adi Laurentiu Tarca in a press release. Tarca has twice been an sbv IMPROVER best performer along with teammate Roberto Romero, both of Wayne State University, School of Medicine. “They provide a robust framework in which to test, compare and scrutinize predictive methodologies with direct applicability in many different areas of biomedicine. The personal benefits of participation are many, but what is most compelling is the access to high-quality data and opportunity to contribute to an open, objective and powerful new approach to the verification of scientific methods and results.”

The Systems Toxicology Computational Challenge, like previous sbv IMPROVER challenges, is led and funded by Philip Morris International Research and Development (PMI R&D). Submissions will be accepted through to spring 2016. Entrants have access to new, high-quality, large scale datasets and will be collaborating with a global community of systems biology experts. Best performers will have the opportunity to contribute to peer-reviewed scientific publications and, most importantly, will become part of a pioneering movement that is working towards a more meaningful understanding of biological systems.

The Systems Toxicology Computational Challenge is open to industrial researchers, academic researchers, independent scientists, data mining specialists and any other interested groups. Participants may be teams or individuals. For more information and registration, visit

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