sbv IMPROVER at a Glance

sbv IMPROVER stands for Systems Biology Verification combined with Industrial Methodology for Process Verification in Research. This approach aims to provide a measure of quality control of industrial research and development by verifying the methods used. The sbv IMPROVER project is a collaborative effort led and funded by PMI Research and Development. For more information please see Nature Biotechnology (2011) or Bioinformatics (2012).

It is different from other scientific crowdsourcing approaches as it focuses on the verification of processes in an industrial context, and not just on basic questions regarding science. The sbv IMPROVER approach allows an organization to benchmark its methods and industrial processes.

Today the scope of sbv IMPROVER is the verification of methods and concepts in systems biology research. However, the scope of the project could be extended to the verification of research processes in other industries such as pharmaceuticals, biotechnology, nutrition and environmental safety, to name a few.

Approach to Challenge Design 



Why you should be Part of sbv IMPROVER

  • Take the opportunity to compete for research grants
  • Receive an independent assessment of your methods
  • Gain access to high quality data
  • Enhance your visibility and gain recognition from an international team of eminent scientists
  • Publish: the results of the challenge and the identity of the best performing entrants will be submitted to a high impact peer reviewed journal
  • Present your results at the sbv IMPROVER Symposium: the best performing entrants will be invited to present their approach at an international symposium

sbv IMPROVER. Verified by you.

The Network Verification Challenge demonstrates just how effective crowd-sourcing can be to build, test and validate complex biological models. We have used the outputs of the first stage of the Network Verification Challenge as the gold-standard for our own text mining evaluation campaign, which is being run as part of BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology). With the Network Verification Challenge now attracting many more participants we are seeing an even greater level of granularity emerge. The robustness of the methodology, together with the high level of activity we are seeing in the challenge, means that the models can be used with confidence in a whole range of applications.

Dr. Fabio Rinaldi
On the Network Verification Challenge

sbv IMPROVER is utilizing the knowledge of the crowd to address specific biological problems. In classifying smokers and non-smokers using gene signature data, we needed a model that could work on independent datasets. To achieve this we applied open source machine learning tools for feature selection and prediction, optimising our algorithm with a cross validation approach.

Dr Sandeep Kumar Dhanda


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